神经氨酸酶抑制剂的分子对接、定量结构-活性关系及全新分子设计
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摘要
神经氨酸酶抑制剂(neuraminidase inhibitors, NIs)是近年来重点研究的一类新型抗流感病毒药物,NIs可选择性地抑制神经氨酸酶(neuraminidase,NA),阻止子代病毒颗粒在宿主细胞中的复制和释放,从而有效地治疗流感和缓解症状。作为目前最有效的一类抗流感药物,NIs的耐药性病例已成逐年上升趋势,因此新型NIs的研发已迫在眉睫。
本文采用计算机辅助药物分子设计(Computer-Aided Drug Design, CADD)中的定量构效关系(Quantitative Structure Activity Relationship, QSAR)和分子对接(docking)对6类单一骨架和2类混合骨架NIs进行系统研究,并取得了以下重要研究成果:
(1) 8个NA的序列比对及其共结晶配体的作用模式分析表明:8个NA序列中完全相同(identity)的残基占19.7%。氢键、静电和疏水是影响NA-NIs相互作用的重要因素,且活性口袋中精氨酸(Arg118、152、292和371)、天冬氨酸(Asp151)、谷氨酸(Glu227)、色氨酸(Trp178)等残基在NA-NIs相互作用中起关键作用。
8类NIs的对接研究,表明氢键、静电和疏水是影响NA-NIs相互作用的重要因素。对接可较准确地反映配体在受体活性口袋中的空间取向,且NA共结晶配体的计算位置与其观测位置之间的均方根偏差(root mean squared diviation,RMSD)都小于1.50?,相似性(Similarity, a measure of similarity between solution coordinates and reference coordinates)在0.72~0.84之间。87个吡咯烷衍生物与8个NA之间的对接得分(total score)与实验活性之间具有显著的相关性,其相关系数r在0.443~0.692 (p<0.001)之间。
(2) 87个吡咯烷衍生物:采用HQSAR和Almond进行QSAR建模,最优模型的R2分别为0.846和0.830、Q2分别为0.670和0.560、R2test分别为0.872和0.856。分别基于共同结构、受体和药效团三种叠合方法,进行CoMFA和CoMSIA分析,得到6个最优QSAR模型的R2、Q2、R2test分别在0.588~0.861、0.437~0.625和0.614~0.875之间。QSAR分析表明氢键对活性的贡献最大,其次是疏水、静电和立体作用,与对接结果一致。基于共同结构建立的CoMFA模型,采用LeapFrog全新药物分子设计,得到8个对接得分和预测活性都较高的新化合物。
(3) 36个硫脲和噻重氮[2,3-α]嘧啶衍生物:HQSAR和基于受体叠合的CoMFA和CoMSIA分析,得到QSAR模型的R2、Q2和R2test分别在0.863~0.899、0.509~0.656和0.558~0.667之间。基于36个NIs构建了8个特征的药效团模型,并基于此模型进行叠合,得到的CoMFA和CoMSIA模型的R2和Q2分别为0.989和0.737、0.470和0.401。采用3D HoVAIFA得到30个多取代硫脲的最优模型,其模型的R2、Q2和R2test分别为0.849、0.724和0.689。采用LeapFrog、UNITY数据库3D搜索及虚拟筛选,分别得到了7个和10个对接得分和预测活性都较高的新化合物。
(4) 40个黄酮衍生物:HQSAR、Almond、CoMFA和CoMSIA(分别基于共同结构、受体和药效团叠合)建模分析,得到8个QSAR的R2、Q2、R2test和Q2ext分别在0.872~0.988、0.420~0.772、0.214~0.935和0.272~0.782之间。利用LeapFrog、UNITY数据库3D搜索及虚拟筛选,分别得到了对接得分和预测活性都较高的8个和6个新化合物。
(5) 68个环己烯衍生物:HQSAR、CoMFA和CoMSIA建模研究,得到5个QSAR模型的R2、Q2、R2test和Q2ext分别在0.847~0.920、0.413~0.652、0.526~0.841和0.531~0.838之间。LeaFrog、UNITY数据库3D搜索及虚拟筛选,分别得到了15个和13个对接得分和预测活性都较高的新化合物。
(6) 45个苯甲酸衍生物:HQSAR和基于药效团叠合所建CoMFA和CoMSIA分析,得到3个QSAR模型的R2、Q2和SEE分别在0.724~0.758、0.418~0.466和0.418~0.466之间。基于药效团模型,UNITY数据库3D搜索及虚拟筛选,得到了对接得分和得分一致性值CScore都较高的20个新化合物。
(7) 38个环戊烷衍生物:采用HQSAR、CoMFA和CoMSIA建模,得到3个QSAR模型的R2、Q2和SEE分别在0.783~0.963、0.256~0.413和0.152~0.375之间。基于药效团模型, UNITY数据库3D搜索和虚拟筛选,得到了对接得分和得分一致性值CScore都较高的12个新化合物。
(8) 124个混合骨架NIs:通过HQSAR、HoVAIFA、Almond、CoMFA和CoMSIA研究,得到7个QSAR模型的R2、Q2、R2test和Q2ext分别在0.775~0.952、0.456~0.818、0.518~0.748和0.516~0.799之间。采用LeapFrog、UNITY数据库3D搜索和虚拟筛选,分别得到了预测活性和对接得分都较高的5个和11个新化合物。
(9) 41个混合骨架NIs:HQSAR、Alomnd、CoMFA和CoMSIA建模分析,得到了4个QSAR模型的R2、Q2、R2test和Q2ext分别在0.661~0.910、0.390~0.554、0.396~0.812和0.300~0.744之间。基于药效团模型,UNITY数据库3D搜索和虚拟筛选,得到了对接得分和CScore值都较高的12个新化合物。
(10) 127个设计的新化合物的ADME性质预测结果,表明所设计的新化合物中约三分之一的化合物都具有良好的渗透能力、蛋白结合亲和力、体积分布以及代谢稳定性。
Recently, neuraminidase inhibitors (NIs) are a focus of new anti-influenza virus drugs. NIs can selectively inhibit neuraminidase (NA), and prevent progeny virosome from replication and release in the host cell, thus take precautions against influenza and the alleviation symptom effectively. As an effective kind of anti-influenza drugs, there is a rising trend toward resistance of NIs due to emergence of new influenza virus. So, it is necessary to research and develop new NIs.
In the dissertation, Quantitative structure-activity relationship (QSAR) and molecular docking in computer-aided drug design (CADD) were employed to systematically investigate six classes with the same skeleton and two classes with different skeletons of NIs. The main conclusions were as follows:
(1) Sequence alignment of 8 NA indicated that the identical residues were 19.7%. Hydrogen bonding, hydrophobic and electrostatic interactions were important factors which had effect on NA-NIs interactions. Moreover, Arginine (118, 152, 292 and 371), Aspartate 151, Glutamate 227, Tryptophan 178 and so on were the key residues in the active site.
Docking results between 8 classes of NIs and NA showed that hydrogen bonding, hydrophobic and electrostatic interactions were key factors which affected NA-NIs interactions. RMSD (root mean squared deviation) between the position calculated and that observed of crystal structure in NA were all smaller than 1.50?, and similarity (a measure of similarity between solution coordinates and reference coordinates) were from 0.72 to 0.84. Docking studies between 8 NA and 87 pyrrolidine derivatives showed that the hydrogen bonding, hydrophobic and electrostatic interactions affected on the activity of NIs. There was a significant correlation between docking score (total scores) and the experimental activities of 87 pyrrolidine derivatives, correlation coefficient r=0.443~0.692 (p<0.001).
(2) 87 pyrrolidine derivatives: R2, Q2 and R2test of HQSAR and Almond models were 0.846 and 0.830, 0.670 and 0.560, 0.872 and 0.856, respectively. R2, Q2 and R2test of CoMFA and CoMSIA (common structures, receptor and pharmacophore superposition) models were from 0.588 to 0.861, from 0.437 to 0.625, and from 0.614 to 0.875, respectively. Based on CoMFA model (common structure alignment), 8 new compounds with high docking score and predicted activity were obtained by LeapFrog.
(3) 36 acyl(thio)urea and thiadiazolo [2,3-a] pyrimidine derivatives: R2, Q2 and R2test of HQSAR, CoMFA and CoMSIA (based on receptor alignment) models were from 0.863 to 0.899, from 0.509 to 0.656, and from 0.558 to 0.667, respectively. At the same time, the pharmacophore model with 8 features was obtained by GALAHAD. Based on pharmacophore superposition, R2 and Q2 of CoMFA and CoMSIA models were 0.989 and 0.737, 0.470 and 0.401, respectively. In addition, R2, Q2 and R2test of HoVAIFA QSAR for 30 acyl(thio)urea derivatives were in turn 0.849, 0.724 and 0.689. 7 and 10 new compounds with high docking scores and predictive activity were obtained by LeapFrog and UNITY 3D search, respectively.
(4) 40 flavonoids derivatives: R2, Q2, R2test and Q2ext of HQSAR, Almond, CoMFA and CoMSIA (common structure, receptor and pharmacophore alignment) models were from 0.872 to 0.988, from 0.420 to 0.772, from 0.214 to 0.935, and from 0.272 to 0.782, respectively. By LeapFrog and UNITY database 3D Search, 8 and 6 new compounds with high docking score and predicted activity were obtained, respectively.
(5) 68 cyclohexene derivatives: R2、Q2、R2test and Q2ext of HQSAR, CoMFA and CoMSIA (pharmacophore and receptor superposition) models were from 0.847 to 0.920, from 0.413 to 0.652, from 0.526 to 0.841, and from 0.531 to 0.838, respectively. 15 and 13 new compounds with high docking score and predicted activity were obtained using LeapFrog and UNITY database 3D search, respectively.
(6) 45 benzoic acid derivatives: R2、Q2 and SEE (standard error of estimate) of HQSAR, CoMFA and CoMSIA models were from 0.724 to 0.758, from 0.418 to 0.466, and from 0.418 to 0.466, respectively. 20 new compounds with high docking scores and CScore values were obtained by UNITY database 3D search and virtual screening.
(7) 38 cyclopentane derivatives: R2, Q2 and SEE of HQSAR, CoMFA and CoMSIA models were from 0.783 to 0.963, from 0.256 to 0.413, and from 0.152 to 0.375, respectively. Based pharmacophore model, 12 new compounds with docking score and docking consistency values CScore were obtained through UNITY database search and virtual screening.
(8) 124 NIs with different skeletons: R2, Q2, R2test and Q2ext of HQSAR, HoVAIFA, Almond, CoMFA and CoMSIA models were from 0.775 to 0.952, from 0.456 to 0.818, from 0.518 to 0.748, and from 0.516 to 0.799, repectively. 5 and 11 new compounds with high predicted activity and docking score were obtained by LeapFrog and UNITY database 3D search, respectively.
(9) 41 NIs with different skeletons: R2, Q2, R2test and Q2ext of HQSAR, Alomnd, CoMFA and CoMSIA, HQAR and CoMSIA models were from 0.661 to 0.910, from 0.390 to 0.554, from 0.396 to 0.812, and from 0.300 to 0.744, respectively. Based pharmacophore model, 12 new compounds with high docking score and CScore values were obtained through UNITY database 3D search and virtual screening.
(10) ADME researches indicated that one third of 127 new designed compounds had good permeation ability, high protein binding affinity, volume distribution and metabolic stability.
本文采用计算机辅助药物分子设计(Computer-Aided Drug Design, CADD)中的定量构效关系(Quantitative Structure Activity Relationship, QSAR)和分子对接(docking)对6类单一骨架和2类混合骨架NIs进行系统研究,并取得了以下重要研究成果:
(1) 8个NA的序列比对及其共结晶配体的作用模式分析表明:8个NA序列中完全相同(identity)的残基占19.7%。氢键、静电和疏水是影响NA-NIs相互作用的重要因素,且活性口袋中精氨酸(Arg118、152、292和371)、天冬氨酸(Asp151)、谷氨酸(Glu227)、色氨酸(Trp178)等残基在NA-NIs相互作用中起关键作用。
8类NIs的对接研究,表明氢键、静电和疏水是影响NA-NIs相互作用的重要因素。对接可较准确地反映配体在受体活性口袋中的空间取向,且NA共结晶配体的计算位置与其观测位置之间的均方根偏差(root mean squared diviation,RMSD)都小于1.50?,相似性(Similarity, a measure of similarity between solution coordinates and reference coordinates)在0.72~0.84之间。87个吡咯烷衍生物与8个NA之间的对接得分(total score)与实验活性之间具有显著的相关性,其相关系数r在0.443~0.692 (p<0.001)之间。
(2) 87个吡咯烷衍生物:采用HQSAR和Almond进行QSAR建模,最优模型的R2分别为0.846和0.830、Q2分别为0.670和0.560、R2test分别为0.872和0.856。分别基于共同结构、受体和药效团三种叠合方法,进行CoMFA和CoMSIA分析,得到6个最优QSAR模型的R2、Q2、R2test分别在0.588~0.861、0.437~0.625和0.614~0.875之间。QSAR分析表明氢键对活性的贡献最大,其次是疏水、静电和立体作用,与对接结果一致。基于共同结构建立的CoMFA模型,采用LeapFrog全新药物分子设计,得到8个对接得分和预测活性都较高的新化合物。
(3) 36个硫脲和噻重氮[2,3-α]嘧啶衍生物:HQSAR和基于受体叠合的CoMFA和CoMSIA分析,得到QSAR模型的R2、Q2和R2test分别在0.863~0.899、0.509~0.656和0.558~0.667之间。基于36个NIs构建了8个特征的药效团模型,并基于此模型进行叠合,得到的CoMFA和CoMSIA模型的R2和Q2分别为0.989和0.737、0.470和0.401。采用3D HoVAIFA得到30个多取代硫脲的最优模型,其模型的R2、Q2和R2test分别为0.849、0.724和0.689。采用LeapFrog、UNITY数据库3D搜索及虚拟筛选,分别得到了7个和10个对接得分和预测活性都较高的新化合物。
(4) 40个黄酮衍生物:HQSAR、Almond、CoMFA和CoMSIA(分别基于共同结构、受体和药效团叠合)建模分析,得到8个QSAR的R2、Q2、R2test和Q2ext分别在0.872~0.988、0.420~0.772、0.214~0.935和0.272~0.782之间。利用LeapFrog、UNITY数据库3D搜索及虚拟筛选,分别得到了对接得分和预测活性都较高的8个和6个新化合物。
(5) 68个环己烯衍生物:HQSAR、CoMFA和CoMSIA建模研究,得到5个QSAR模型的R2、Q2、R2test和Q2ext分别在0.847~0.920、0.413~0.652、0.526~0.841和0.531~0.838之间。LeaFrog、UNITY数据库3D搜索及虚拟筛选,分别得到了15个和13个对接得分和预测活性都较高的新化合物。
(6) 45个苯甲酸衍生物:HQSAR和基于药效团叠合所建CoMFA和CoMSIA分析,得到3个QSAR模型的R2、Q2和SEE分别在0.724~0.758、0.418~0.466和0.418~0.466之间。基于药效团模型,UNITY数据库3D搜索及虚拟筛选,得到了对接得分和得分一致性值CScore都较高的20个新化合物。
(7) 38个环戊烷衍生物:采用HQSAR、CoMFA和CoMSIA建模,得到3个QSAR模型的R2、Q2和SEE分别在0.783~0.963、0.256~0.413和0.152~0.375之间。基于药效团模型, UNITY数据库3D搜索和虚拟筛选,得到了对接得分和得分一致性值CScore都较高的12个新化合物。
(8) 124个混合骨架NIs:通过HQSAR、HoVAIFA、Almond、CoMFA和CoMSIA研究,得到7个QSAR模型的R2、Q2、R2test和Q2ext分别在0.775~0.952、0.456~0.818、0.518~0.748和0.516~0.799之间。采用LeapFrog、UNITY数据库3D搜索和虚拟筛选,分别得到了预测活性和对接得分都较高的5个和11个新化合物。
(9) 41个混合骨架NIs:HQSAR、Alomnd、CoMFA和CoMSIA建模分析,得到了4个QSAR模型的R2、Q2、R2test和Q2ext分别在0.661~0.910、0.390~0.554、0.396~0.812和0.300~0.744之间。基于药效团模型,UNITY数据库3D搜索和虚拟筛选,得到了对接得分和CScore值都较高的12个新化合物。
(10) 127个设计的新化合物的ADME性质预测结果,表明所设计的新化合物中约三分之一的化合物都具有良好的渗透能力、蛋白结合亲和力、体积分布以及代谢稳定性。
Recently, neuraminidase inhibitors (NIs) are a focus of new anti-influenza virus drugs. NIs can selectively inhibit neuraminidase (NA), and prevent progeny virosome from replication and release in the host cell, thus take precautions against influenza and the alleviation symptom effectively. As an effective kind of anti-influenza drugs, there is a rising trend toward resistance of NIs due to emergence of new influenza virus. So, it is necessary to research and develop new NIs.
In the dissertation, Quantitative structure-activity relationship (QSAR) and molecular docking in computer-aided drug design (CADD) were employed to systematically investigate six classes with the same skeleton and two classes with different skeletons of NIs. The main conclusions were as follows:
(1) Sequence alignment of 8 NA indicated that the identical residues were 19.7%. Hydrogen bonding, hydrophobic and electrostatic interactions were important factors which had effect on NA-NIs interactions. Moreover, Arginine (118, 152, 292 and 371), Aspartate 151, Glutamate 227, Tryptophan 178 and so on were the key residues in the active site.
Docking results between 8 classes of NIs and NA showed that hydrogen bonding, hydrophobic and electrostatic interactions were key factors which affected NA-NIs interactions. RMSD (root mean squared deviation) between the position calculated and that observed of crystal structure in NA were all smaller than 1.50?, and similarity (a measure of similarity between solution coordinates and reference coordinates) were from 0.72 to 0.84. Docking studies between 8 NA and 87 pyrrolidine derivatives showed that the hydrogen bonding, hydrophobic and electrostatic interactions affected on the activity of NIs. There was a significant correlation between docking score (total scores) and the experimental activities of 87 pyrrolidine derivatives, correlation coefficient r=0.443~0.692 (p<0.001).
(2) 87 pyrrolidine derivatives: R2, Q2 and R2test of HQSAR and Almond models were 0.846 and 0.830, 0.670 and 0.560, 0.872 and 0.856, respectively. R2, Q2 and R2test of CoMFA and CoMSIA (common structures, receptor and pharmacophore superposition) models were from 0.588 to 0.861, from 0.437 to 0.625, and from 0.614 to 0.875, respectively. Based on CoMFA model (common structure alignment), 8 new compounds with high docking score and predicted activity were obtained by LeapFrog.
(3) 36 acyl(thio)urea and thiadiazolo [2,3-a] pyrimidine derivatives: R2, Q2 and R2test of HQSAR, CoMFA and CoMSIA (based on receptor alignment) models were from 0.863 to 0.899, from 0.509 to 0.656, and from 0.558 to 0.667, respectively. At the same time, the pharmacophore model with 8 features was obtained by GALAHAD. Based on pharmacophore superposition, R2 and Q2 of CoMFA and CoMSIA models were 0.989 and 0.737, 0.470 and 0.401, respectively. In addition, R2, Q2 and R2test of HoVAIFA QSAR for 30 acyl(thio)urea derivatives were in turn 0.849, 0.724 and 0.689. 7 and 10 new compounds with high docking scores and predictive activity were obtained by LeapFrog and UNITY 3D search, respectively.
(4) 40 flavonoids derivatives: R2, Q2, R2test and Q2ext of HQSAR, Almond, CoMFA and CoMSIA (common structure, receptor and pharmacophore alignment) models were from 0.872 to 0.988, from 0.420 to 0.772, from 0.214 to 0.935, and from 0.272 to 0.782, respectively. By LeapFrog and UNITY database 3D Search, 8 and 6 new compounds with high docking score and predicted activity were obtained, respectively.
(5) 68 cyclohexene derivatives: R2、Q2、R2test and Q2ext of HQSAR, CoMFA and CoMSIA (pharmacophore and receptor superposition) models were from 0.847 to 0.920, from 0.413 to 0.652, from 0.526 to 0.841, and from 0.531 to 0.838, respectively. 15 and 13 new compounds with high docking score and predicted activity were obtained using LeapFrog and UNITY database 3D search, respectively.
(6) 45 benzoic acid derivatives: R2、Q2 and SEE (standard error of estimate) of HQSAR, CoMFA and CoMSIA models were from 0.724 to 0.758, from 0.418 to 0.466, and from 0.418 to 0.466, respectively. 20 new compounds with high docking scores and CScore values were obtained by UNITY database 3D search and virtual screening.
(7) 38 cyclopentane derivatives: R2, Q2 and SEE of HQSAR, CoMFA and CoMSIA models were from 0.783 to 0.963, from 0.256 to 0.413, and from 0.152 to 0.375, respectively. Based pharmacophore model, 12 new compounds with docking score and docking consistency values CScore were obtained through UNITY database search and virtual screening.
(8) 124 NIs with different skeletons: R2, Q2, R2test and Q2ext of HQSAR, HoVAIFA, Almond, CoMFA and CoMSIA models were from 0.775 to 0.952, from 0.456 to 0.818, from 0.518 to 0.748, and from 0.516 to 0.799, repectively. 5 and 11 new compounds with high predicted activity and docking score were obtained by LeapFrog and UNITY database 3D search, respectively.
(9) 41 NIs with different skeletons: R2, Q2, R2test and Q2ext of HQSAR, Alomnd, CoMFA and CoMSIA, HQAR and CoMSIA models were from 0.661 to 0.910, from 0.390 to 0.554, from 0.396 to 0.812, and from 0.300 to 0.744, respectively. Based pharmacophore model, 12 new compounds with high docking score and CScore values were obtained through UNITY database 3D search and virtual screening.
(10) ADME researches indicated that one third of 127 new designed compounds had good permeation ability, high protein binding affinity, volume distribution and metabolic stability.
引文
[1] S. Toovey. Influenza-associated central nervous system dysfunction: a literature review [J]. Travel Medicine and Infectious Disease. 2008, 6: 114-124.
[2] J.L. McKimm-Breschkin. Resistance of influenza viruses to neuraminidase inhibitors—a review [J]. Antiviral Research. 2000, 47: 1-17.
[3] L.V. Gubareva, L. Kaiser, F.G. Hayden. Influenza virus neuraminidase inhibitors [J]. Lancet 2000, 355: 827-835.
[4] O. Ferraris, B. Lina. Mutations of neuraminidase implicated in neuraminidase inhibitors resistance [J]. Journal of Clinical Virology. 2008, 41: 13-19.
[5] World Health Organization (WHO), Bull [J]. WHO. 1980, 58: 585.
[6] W.J. Brouillette, S.N. Bajpai, S.M. Ali, S.E. Velu, V.R. Atigadda, B.S. Lommer, J.B. Finley, M. Luo, G.M. Air. Pyrrolidinobenzoic Acid Inhibitors of Influenza Virus Neuraminidase: Modifications of Essential Pyrrolidinone Ring Substituents [J]. Bioorganic & Medicinal Chemistry. 2003, 11: 2739-2749.
[7] M. M. Mukhtar, S. T. Rasool, D. Song, C. Zhu, Q. Hao, Y. Zhu, J. Wu. Origin of highly pathogenic H5N1 avian influenza virus in China and genetic characterization of donor and recipient viruses [J]. Journal of General Virology Virol. 2007, 88: 3094-3099.
[8] N. Shirvan, M. Moradi, M. Aminian, R. Madani. Preparation of Neuraminidase-Specific Antiserum from the H9N2 Subtype of Avian Influenza Virus [J]. Turkish Journal of Veterinary andAnimal Science. 2007, 31: 219-223.
[9] Q. Zhang, J. Yang, K. Liang, L. Feng, S. Li, J. Wan, X. Xu, G. Yang, D. Liu, S. Yang. Binding interaction analysis of the active site and its inhibitors for neuraminidase (N1 subtype) of human influenza virus by the integration of molecular docking, FMO calculation and 3D-QSAR CoMFA modeling [J]. Journal of Chemical Information and Modeling. 2008, 48: 1802-1812.
[10] S.U. Schnitzler, P. Schnitzler. An update on swine-origin influenza virus A/H1N1: a review [J]. Virus Genes. 2009, 39: 279-292.
[11] Q.S. Du, S.Q. Wang, R.B. Huang, K.C. Chou. Computational 3D structures of drug-targeting proteins in the 2009-H1N1 influenza a virus [J]. Chemical Physics Letters. 2010, 485: 191-195.
[12] Centers for Disease Control and Prevention. Prevention and control of influenza: recommendations of the Advisory Committee on Immunization Practices (ACIP. MMWR:Morbidity and Mortality Weekly Report [J] 1999, 48: 1-28.
[13] T. Betakova. M2 Protein–A Proton Channel of Influenza A Virus [J]. Current Pharmaceutical Design. 2007, 13: 3231-3235.
[14] G.J. Galasso. An assessment of antiviral drugs for the management of infectious diseases in human [J]. Antiviral Research. 1981, 1: 73-96.
[15] A.B. Tuzikov, N.E. Byramova, N.V. Bovin, A.S. Gambaryan, M.N. Matrosovich. Monovalent and polymeric 5N-thioacetamido sialosides as tightly-bound receptor analogs of influenza viruses [J]. Antiviral Research. 1997, 33: 129-134.
[16] T. Hatta, K. Takai, S. Nakada, T. Yokota, H. Takaku. Specific inhibition of influenza virus RNA polymerase and nucleoprotein genes expression by liposomally endocapsulated antisense phosphorothioate oligonucleotides in clone 76 cells [J]. Biochemical and Biophysical Research Communications. 1997, 232: 545-549.
[17] R.F. Service Researchers seek new weapon against the flu [J]. Science. 1997, 275: 756-757.
[18] J.S. Oxford, P. Novelli , A. Sefton, R. Lambkin. New millennium antivirals against pandemic and epidemic influenza: the neuraminidase inhibitors [J]. Antivir Chemistry & Chemotherapy. 2002, 13: 205-217.
[19] D.P. Calfee, F.G. Hayden. New approaches to influenza chemotherapy neuraminidase inhibitors [J]. Drugs. 1998, 56: 537-553.
[20] W.G. Laver, N. Bischofberger, R.G. Webster. Disarming flu viruses [J]. Scientific American. 1999, 280: 78-82.
[21] P.J. Gavin, R.B. Thomson. Review of rapid diagnostic tests for influenza [J]. Clinical and Applied Immunology Reviews. 2003, 4: 151-172.
[22] S. Schultz-Cherry, V.S. Hinshaw. Influenza virus neuraminidase activates latent transforming growth factor beta [J]. Journal of Virology. 1996, 70: 8624-8629.
[23] M.N. Janakiraman, C.L. White, W.G. Laver, G.M. Air, M. Luo. Structure of influenza virus neuraminidase B/Lee/40 complexed with sialic acid and a dehydro analogue at 1.8? resolution: implication for the catalytic mechanism [J]. Biochemistry. 1994, 33: 8172-8179.
[24] J.N. Varghese, J.L. Mckimm-Breschkin, J.B. Caldwell, A.A. Kortt, P.M. Colman. The structure of the complex between influenza virus neuraminidase and sialic acid, the viral receptor [J]. Proteins: Structure, Function, and Genetics. 1992, 14: 327-332.
[25] P. Bossart-Whitaker, M. Carson, Y.S. Babu, C.D. Smith, W.G. Laver, G.M. Air. Threedimensional structure of influenza A N9 neuraminidase and its complex with the inhibitor 2-deoxy-2,3-dehydro-N-acetyl neuraminic acid [J]. Journal of Molecular Biology. 1993, 232: 1069-1083.
[26] N.R. Taylor, A. Cleasby, O. Singh, T. Skarzynski, A.J. Wonacott, P.W. Smith, S.L. Sollis, P.D. Howes, P.C. Cherry, R. Bethell, P. Colman, J. Varghese. Dihydropyrancarbox-amides related to zanamivir: a new series of inhibitors of influ-enza virus sialidases. 2. crystallographic and molecular modeling study of complexes of 4-amino-4H-pyran-6-carboxamides and sialidase from influenza virus types A and B [J]. Journal of Medicianl Chemistry. 1998, 41: 798-807.
[27] D. Low. Reducing antibiotic use in influenza: challenges and rewards. Clinical Microbiology and Infection [J]. 2008, 14: 298-306.
[28] T. Wang, R.C. Wade. Comparative Binding Energy (COMBINE) Analysis of Influenza Neuraminidase-Inhibitor Complexes [J]. Journal of Medicinal Chemistry. 2001, 44: 961-971.
[29] A.U. Khan, S. Shakil, S.K. Lal. Effi cacy of neuraminidase (NA) inhibitors against H1N1 strains of different geographical regions: an in silico approach [J]. Indian Journal of Microbiology. 2009, 49: 370-376.
[30] J. Zhang, K. Yu, W. Zhua, H. Jiang. Neuraminidase pharmacophore model derived from diverse classes of inhibitors [J]. Bioorganic & Medicinal Chemistry Letters. 2006, 16: 3009-3014.
[31] A.M.A. Hammad, M.O. Taha. Pharmacophore Modeling, Quantitative Structure-Activity Relationship Analysis, and Shape-Complemented in Silico Screening Allow Access to Novel Influenza Neuraminidase Inhibitors [J]. Journal of Chemical Information and Modeling. 2009, 49: 978-996.
[32] A.T. García-Sosa, S. Sild, U. Maran. Docking and Virtual Screening Using Distributed Grid Technology [J]. QSAR Combinatorial Science. 28, 2009, 8: 815-821.
[33] X. Yi, Z. Guo, F.M. Chu. Study on Molecular Mechanism and 3D-QSAR of Influenza Neuraminidase Inhibitors [J]. Bioorganic & Medicinal Chemistry. 2003, 11: 1465-1474.
[34] R.P. Verma, C.A. Hansch. QSAR study on influenza neuraminidase inhibitors [J]. Bioorganic & Medicinal Chemistry. 2006, 14: 982-996.
[35] P.C. Nair, M.E. Sobhia. Quantitative structure activity relationship studies on thiourea analogues as influenza virus neuraminidase inhibitors [J]. European Journal of Medicinal Chemistry. 2008, 43: 293-299.
[36] Q.S. Du, R.B. Huang, Y.T. Wei, L.Q. Du, K.C. Chou. Multiple Field Three Dimensional Quantitative Structure-Activity Relationship (MF-3D-QSAR) [J]. Journal of Computational Chemistry. 2008, 29: 211-219.
[37] W.J. Lü, Y.L. Chen, W.P. Ma, X.Y. Zhang, F. Luan, M.C. Liu, X.G. Chen, Z.D. Hu. QSAR study of neuraminidase inhibitors based on heuristic method and radial basis function network [J]. European Journal of Medicinal Chemistry. 2008, 43: 569-576.
[38] Q.S. Du, R.B. Huang, Y.T. Wei, Z.W. Pang, L.Q. Du, K.C. Chou. Fragment-Based Quantitative Structure–Activity Relationship (FB-QSAR) for Fragment-Based Drug Design [J]. Journal of Computational Chemistry. 2009, 30: 295-304.
[39] W.M. Kati, D. Montgomery, C. Maring, V.S. Stoll, V. Giranda, X. Chen, W. Graeme Laver, W. Kohlbrenner, D.W. Norbeck. Novelα- andβ-Amino Acid Inhibitors of Influenza Virus Neuraminidase [J]. Antimicrobial Agents and Chemotherapy. 2001, 45: 2563-2570.
[40] J. Zhang, W. Xu, A. Liu, G. Du. Design, Synthesis, and Preliminary Evaluation of New Pyrrolidine Derivatives as Neuraminidase Inhibitors [J]. Medicinal Chemistry. 2008, 4, 206-209.
[41] V. Stoll, K.D. Stewart, C.J. Maring, S. Muchmore, V. Giranda, Y.Y. Gu, G. Wang, Y. Chen, M. Sun, C. Zhao, A.L. Kennedy, D.L. Madigan, Y. Xu, A. Saldivar, W. Kati, G. Laver, T. Sowin, H.L. Sham, J. Greer, D. Kempf. Influenza Neuraminidase Inhibitors: Structure-Based Design of a Novel Inhibitor Series [J]. Biochemistry. 2003, 42, 718-727.
[42] Y. Sudhakar Babu, P. Chand, S. Bantia, P. Kotian, A. Dehghani, Y. El-Kattan, T.H. Lin, T. L. Hutchison, A. J. Elliott, C. D. Parker, S.L. Ananth, L.L. Horn, G.W. Laver, J.A. Montgomery. BCX-1812 (RWJ-270201): Discovery of a Novel, Highly Potent, Orally Active, and Selective Influenza Neuraminidase Inhibitor through Structure-Based Drug Design [J]. Journal of Medicianl Chemistry. 2000, 43: 3482-3486.
[43] P. Chand, P.L. Kotian, A. Dehghani, Y. El-Kattan, T.H. Lin, T.L. Hutchison, Y. Sudhakar Babu, S. Bantia, A.J. Elliott, J.A. Montgomery. Systematic Structure-Based Design and Stereoselective Synthesis of Novel Multisubstituted Cyclopentane Derivatives with Potent Antiinfluenza Activity [J]. Journal of Medicianl Chemistry. 2001, 44: 4379-4392.
[44] K. Miki, T. Nagai, K. Suzuki, R. Tsujimura, K. Koyama, K. Kinoshita, K. Furuhata, H. Yamada, K. Takahashi. Anti-influenza virus activity of biflavonoids [J]. Bioorganic & Medicinal Chemistry Letters. 2007, 17: 772-775.
[45] J.J. Shie, J.M. Fang, S.Y. Wang, K.C. Tsai, Y.S.E. Cheng, A.S. Yang, S.C. Hsiao, C.Y. Su, C.H. Wong. Synthesis of Tamiflu and its Phosphonate Congeners Possessing Potent Anti-Influenza Activity [J]. Journal of the American Chemical Society. 2007, 129: 11892-11893.
[46] M. Hochgürtel, R. Biesinger, H. Kroth, D. Piecha, M.W. Hofmann, S. Krause, O. Schaaf, C. Nicolau, A.V. Eliseev. Ketones as Building Blocks for Dynamic Combinatorial Libraries: Highly Active Neuraminidase Inhibitors Generated via Selection Pressure of the Biological Target [J]. Journal of Medicianl Chemistry. 2003, 46: 356-358.
[47] A.M.A. Hammad, F.U. Afifi, M.O. Taha. Combining docking, scoring and molecular field analyses to probe influenza neuraminidase–ligand interactions [J]. Journal of MolecularGraphics and Modelling. 2007, 26: 443-456.
[48] C. Sangma, S. Hannongbua. Structural information and computational methods used in design of neuraminidase inhibitors [J]. Current Computer-Aided Drug Design. 2007, 3: 113-132.
[49] C. Hansch, R.M. Muir, T. Fujita, P.P. Maloney, F. Geiger, M. Streich. The Correlation of biological activity of plant growth regulators and chloromycetin derivatives with hammett constants and partition coefficients [J]. Journal of the American Chemical Society. 1963, 85: 2817.
[50] C.Hansch, T.Fujita. F-σ-πanalysis. a method for the correlation of biological activity and chemical structure [J]. Journal of the American Chemical Society. 1964, 86: 1616.
[51] I.B. Bersuker, A.S. Dimoglo, M.Y. Gorbachov. The Electron-topological approach in investigation of structure-activity-relation-inhibition of thymidine phosphorylase by uracil derivatives [J]. Bioorgan Khim. 1987, 13:38.
[52] I.B. Bersuker, A.S. Dimoglo, M.Y. Gorbachov, P.P. Maloney. Origin of musk fragrance activity-the electron-topologic approach [J]. New Journal of Chemistry. 1991, 15: 307.
[53] S.H. Free, J.W. Wilson. A mathematical contribution to structure-activity studies [J]. Journal of Medicianl Chemistry. 1964, 7: 395.
[54] T. Aoyama, Y. Suzuki, H. Ichikawa. Neural nerworks applied to pharmaceutical problems. 1. method and application to decision making [J]. Chemical & Pharmaceutical Bulletin. 1989, 37: 2558.
[55] G.M. Crippen. Distance geometry approach to rationalizing binding date [J]. Journal of Medicianl Chemistry. 1979, 22: 988.
[56] G.M. Crippen. Quantitative structure-activity-ralationships by distance geometry-systematic analysis of dihydrofolate-reductase inhibitors [J]. Journal of Medicianl Chemistry. 1980, 23: 599.
[57] G.M.Crippen. Quantitative structure-activity-relationships by distance geometry- thyroxine binding-site [J]. Journal of Medicianl Chemistry. 1981, 24: 198.
[58] L.G. Boulu, G.M. Crippen, H.A. Barton, H. Kwon. M.A. Marletta. Voronoi binding-site model of a polycyclic aromatic hydrocarbon binding-protein [J]. Journal of Medicianl Chemistry. 1990, 33:771.
[59] A.J. Hopfiger. A QSAR investigation of dihydrofolate-reductase inhibition by baker triazines based upon molecular shape analysis [J]. Journal of the American Chemical Society. 1980, 102:7196.
[60] A.J. Hopfiger, R. Potenzone. Ames test and anti-tumor activity of 1-(X-phenyl)-3, 3-dialkyltriazenes-quantitative stucture activity studies based upon molecular sharp analysis[J]. Molecular Pharmacology. 1982, 21: 187.
[61] R.D. Cramer III, D.E. Patterson, J.D. Bunce. Comparative molecular field analysis (CoMFA. 1. Effect of shape on binding of steroids to carrier proteins [J]. Journal of the Ameican Chemical Society. 1988, 110: 5959-5967.
[62] A.N. Jain, K. Koile, D. Chapman. COMPASS-predicting biological-activities from molecular-surface properties-performance comparisons on a steroid benchmark [J]. Journal of Medicianl Chemistry. 1994, 37:2315.
[63] A.N. Jain, T.G. Dietterich, R.H. Lathrop, D. Chapman. COMPASS-a sharp-based machine learning tool for drug design [J]. Journal of Computer-Aided Molecular Design. 1994, 8: 635.
[64] G. Klebe, U. Abraham. Comparative molecular similarity index analysis (CoMSIA to study hydrogen bonding properties and to score combinatorial libraries [J]. Journal of Computer-Aid Molecular Design. 1999, 13: 1-10.
[65] G. Klebe, U. Abraham, T. Mietzner. Molecular similarity indexes in a comparative analysis (CoMSIA of drug molecules to correlate and predict their biological activity [J]. Journal of Medicinal Chemistry. 1994, 37: 4310-4146.
[66] M. B?hm, J. Sturzebecher, G. Klebe. Three-dimensional quantitative structure-activity relationship analyses using comparative molecular field analysis and comparative molecular similarity indices analysis to elucidate selectivity differences of inhibitors binding to trypsin, thrombin, and factor Xa [J]. Journal of Medicinal Chemistry. 1999, 42: 458-477.
[67] P. Benedetti, R. Mannhold, G. Cruciani, G. Ottaviani. GRIND/ALMOND investigations on CysLT1 receptor antagonists of the quinolinyl(bridged)aryl type [J]. Bioorganic & Medicinal Chemistry. 2004, 12: 3607-3617.
[68] G. Ermondi, S. Visentin, G. Caron. GRIND-based 3D-QSAR and CoMFA to investigate topics dominated by hydrophobic interactions: The case of hERG Kt channel blockers [J]. European Journal of Medicinal Chemistry. 2009, 44: 1926-1932.
[69] H. Kubinyi. QSAR and 3D QSAR in drug design [J]. Part 1: Methodology. Drug Discovery Today. 1997, 2: 457-467.
[70] A.J. Hopfinger, S. Wang, J.S. Tokarski, B. Jin, M. Albuquerque, P.J. Madhav, C. Duraiswami. Construction of 3D-QSAR models using the 4D-QSAR analysis formalism [J]. Journal of the Ameican Chemical Society. 1997, 119: 10509-10524.
[71] M.G. Albuquerque, E.J. Barreiro, R.B. de Alencastro, A.J. Hopfinger. Four-dimensional quantitative structure-activity relationship analysis of a series of interphenylene 7-oxabicycloheptane oxazole thromboxane A2 receptor antagonists [J]. Journal of Chemical Information and Computer Sciences. 1998, 38: 925-938.
[72] A. Vedani, H. Briem, M. Dobler, H. Dollinger, D.R. McMasters. Multiple conformation and protonation state representation in 4D-QSAR: The neurokinin-1 receptor system [J]. Journal of Medicinal Chemistry. 2000, 43: 4416-4427.
[73] A. Vedani, M. Dober. 5D QSAR: The key for simulating induced fit [J]. Journal of Medicinal Chemistry. 2002, 45: 2139-2149.
[74] A. Vedani, M. Dobler. Multi-dimentinal QSAR in drug research. Predicting binding affinities, toxicity and pharmacokinetic parameters [J]. Progrss in Drug Research. 2000, 55: 105-135.
[75] A. Vedani, M. Dobler, M.A. Lill. Combining protein modeling and 6D-QSAR. Simulating the binding of structurally diverse ligands to the estrogen receptor [J]. Journal of Medicinal Chemistry. 2005, 48: 3700-3703.
[76] E.M. Krovat, T. Steindl, T. Langer, Recent Advances in Docking and Scoring [J]. Current Computer-Aided Drug Design. 2005, 1, 93-102.
[77] D. E. Koshland. Application of a Theory of Enzyme Specificity to Protein Synthesis [J]. Proceeding of the National Academy Sciences. USA. 1958, 44: 98-104.
[78] L.P. Ehrich, M. Nilges, R.C. Wade. The impact of protein flexibility on protein-protein docking [J]. PROTEINS: Structure, Function, and Bioinformatics. 2005, 58: 126-133.
[79] J. Meiler, D. Baker. ROSETTALIGAND: protein-small molecule with full side-chain flexibility [J]. PROTEINS: Structure, Function, and Bioinformatics. 2006, 65: 538-548.
[80] R. Abagyan, M. Totrov. High-Throughput docking for lead generation [J]. Current Opinion in Chemical Biology. 2002, 5: 375-382.
[81] F. Jing, S.H. Kim. Soft docking: matching of molecular surface cubes [J]. Journal of Molecular Biology. 1991, 219: 79.
[82] I. D. Kuntz. Structure-based Strategies for Drug Design and Discovery [J]. Science. 1992, 257: 1078-1082.
[83] D.S. Goodsell, A.J. Olson. Automated docking of substrates to proteins by simulated annealing [J]. Proteins. 1990, 8: 195-202.
[84] I.D. Kuntz, J.M. Blaney, S.J. Oatley, R. Langridge, T.E. Ferrin A. geometric approach to macromolecule-ligand interactions [J]. Journal of Molecular Biology. 1982, 161: 269-288.
[85] A.J. Olson, D.S. Goodsell. Automated docking and the search for HIV protease inhibitors [J].SAR and QSAR in Environmental Research. 1998, 8: 273-285.
[86] G.M. Morris, D.S. Goodsell, R.S. Halliday, R. Huey, W.E. Hart, R.K. Beiew, A.J. Olson. Automated Docking Using a Lamarekian Genetic Algorithm and Empirical Binding Free Energy Function [J]. Journal of Computational Chemistry. 1998, 19: 1639-1662.
[87] G.M. Morris, R. Huey, W. Lindstrom, M.F. Sanner, R.K. Belew, D.S. Goodsell, A.J. Olson.AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility [J]. Journal of Computational Chemistry. 2009, 30: 2785-2791.
[88] H.J. B?hm. The computer program LUDI: A new method for the de novo design of enzyme inhibitors [J]. Journal of Computer-Aided Molecular Design. 1992, 6: 61-78.
[89] M. Rarey, B. Kramer, T. Lengauer. Docking of hydrophobic ligand with interaction-based matching algorithms [J]. Bioinformatics. 1999, 15: 243-250.
[90] Affinity User Guide [M]. MSI Inc., San Diego. USA . 2002.
[91] B.A. Luty, Z.R. Wassennan, P.F.W. Stouten, C.N. Hodge, M. Zacharias, J.A. McCammon. A Molecular Mechanics/Grid Method for Evaluation of Ligand-Receptor Interactions [J]. Journal of Computational Chemistry. 1995, 16: 454-464.
[92] P.F.W. Stouten, C.Frommel, H. Nakamura, C.Sander. An Effective Solvation Term Based on Atomic Occupancies for Use in Protein Simulations [J]. Molecular Simulattion. 1993, 10: 97-120.
[93] M. Stahl, H.J. B?hm. Development of Filter Functions for Protein-Ligand Docking [J]. Journal of Molecular Graphics and Modelling. 1998,16:121-132.
[94] SYBYL, version 7.3 [M], Tripos Associates, St. Louis, MO. URL www.tripos.com.
[95] R. Judson. Genetic Algorithms and Their Use in Chemistry [J]. In: Lipkowitz K B, Boyd D B, ed. Reviews in Computational Chemistry, Vol 10. New York: VCH Publishers, 1997.
[96] A.N. Jain. Surflex: Fully Automatic Flexible Molecular Docking Using a Molecular Similarity-Based Search Engine [J]. Journal of Medicinal Chemistry. 2003, 46, 499-511.
[97] T.A. Pham, A.J. Jain. Parameter Estimation for Scoring Protein-Ligand Interactions Using Negative Training Data [J]. Journal of Medicinal Chemistry. 2006, 49: 5856-5868.
[98] A.N. Jain, Virtual screening in lead discovery and optimization [J]. Current Opinion in Drug Discovery & Development. 2004, 7: 396-403.
[99] A.N. Jain. Scoring noncovalent protein-ligand interactions: A continuous differentiable function tuned to compute binding affinities [J]. Journal of Computer-Aided Molecular Design. 1996, 10: 427-440.
[100] E. Kellenberger, J. Rodrigo, P. Muller, D. Rognan. Comparative Evaluation of Eight Docking Tools for Docking and Virtual Screening Accuracy [J]. PROTEINS: Structure, Function, and Bioinformatics. 2004, 57: 225-242.
[101] M. Krier, J.X. de Araújo-Júnior, M. Schmitt, J. Duranton, H. Justiano-Basaran, C. Lugnier, J.J. Bourguignon, D. Rognan. Design of Small-Sized Libraries by Combinatorial Assembly of Linkers and Functional Groups to a Given Scaffold: Application to the Structure-Based Optimization of a Phosphodiesterase 4 Inhibitor [J]. Journal of Medicinal Chemistry. 2005, 48:3816-3822.
[102] C. Bissantz, G. Folkers, D. Rognan. Protein-based virtual screening of chemical databases. 1. Evaluation of different docking/scoring combinations [J]. Journal of Medicinal Chemistry. 2000, 43: 4759-4767.
[103] More publications at: http://www.biopharmics.com/publications.html.
[104] A. N. Jain. Effects of protein conformation in docking: improved pose prediction through protein pocket adaptation [J]. J Comput Aided Mol Des. 2009, 23: 355-374.
[105] T.A. Pham, A.N. Jain. Customizing scoring functions for docking [J]. Journal of Computer-Aided Molecular Design. 2008, 22:269-286.
[106] S. Reddy, S.P. Pati, P.P. Kumar, H.N. Pradeep, G.N. Sastry. Virtual Screening in Drug Discovery– A Computational Perspective [J]. Current Protein and Peptide Science. 2007, 8: 329-351
[107] J. Ruppert, W. Welch, A.N. Jain. Automatic identification and representation of protein binding sites for molecular docking [J]. Protein Science. 1997, 6: 524-533.
[108] A.N. Jain. Scoring noncovalent protein-ligand interactions: A continuous differentiable function tuned to compute binding affinities [J]. Journal of Computer-Aided Molecular Design. 1996, 10: 427-440.
[109] W. Welch, J. Ruppert, A.N. Jain. Hammerhead: Fast, fully automated docking of flexible ligands to protein binding sites. Chemical Biology. [J] 1996, 3, 449-462.
[110] R.D. Clark, A. Strizhev, J.M. Leonard, J.F. Blake, J.B. Matthew. Consensus scoring for ligand/protein interactions [J]. Journal of Molecular Graphics and Modelling. 2002, 20: 281.
[111] H.J. B?hm. The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure [J]. Journal of Computer-Aided Molecular Design. 1994, 8: 243-256.
[112] M. Rarey, B. Kramer, T. Lengauer, G. Klebe. A fast flexible docking method using an incremental construction algorithm [J]. Journal of Molecular Biology. 1996, 261, 470-489.
[113] R. Wang, Y. Lu, S. Wang. Comparative Evaluation of 11 Scoring Functions for Molecular Docking [J]. Journal of Medicinal Chemistry. 2003, 46: 2287-2303.
[114] G. Jones, P. Willet, R.C. Glen, A.R. Leach, R. Taylor. Development and validation of a genetic algorithm for flexible docking [J]. Journal of Molecular Biology. 1997, 267: 727.
[115] I. Muegge, Y.C. Martin.A general and fast scoring function for protein-ligand interactions: a simplified potential approach [J]. Journal of Medicinal Chemistry. 1999, 42: 791-804.
[116] I.D. Kuntz, J.M. Blaney, S.J. Oatley, R. Langridge, T.E. Ferrin. A geometric approach to macromolecule-ligand interactions [J]. Journal of Molecular Biology. 1982, 161: 269-288.
[117] M.D. Eldridge, C.W. Murray, T.R. Auton, G.V. Paolini, R.P. Mee. Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes [J]. Journal of Computer-Aided Molecular Design. 1997, 11: 425-445.
[118] S.T. Louis. HQSAR is the product of Tripos Inc [M]. Molecular pharmacology. 1997.
[119] A.A. San, S.J. Cho, H. Cho. HQSAR study of microsomal prostaglandin E-2 synthase (mPGES-1) inhibitors [J]. Bulletin of the Korean Chemical Society. 2006, 27: 1531-1536.
[120] K. Srikanth, P.C. Nair, M.E. Sobhia. Probing the structural and topological requirements for CCR2 antagonism: Holographic QSAR for indolopiperidine derivatives [J]. Bioorganic & Medicinal chemistry letters. 2008, 18:1450-1456.
[121] L.G. Ferreira, A. Leitao, C.A Montanari, A.D Andricopulo. Hologram quantitative structure-activity relationships for a class of inhibitors of HIV-1 protease [J]. Letters in Drug Design & Discovery. 2007, 4: 356-364.
[122] T.L Moda, C.A Montanari, A.D Andricopulo. In silico prediction of human plasma protein binding using hologram QSAR [J]. Letter in Drug Design & Discovery. 2007, 4: 502-509.
[123] N. Masahiro. Toxicity prediction of chemicals base on structure-activity relationships [J]. Toxicology Letters. 1998, 103: 627-629.
[124] P. Zhou, F.F. Tian, Z.L. Li. Three dimensional holographic vector of atomic interaction field (3D-HoVIAF) [J]. Chemometr. Intell. Lab. Syst. DOI: 10.1016/ j.chemolab.2006.10.002.
[125] Z. Li, J. Sun, G. Liang. On three-dimensional holographic vector of atomic interaction field analysis for influenza neuraminidase inhibitors [J]. Chemical Biology & Drug Design. 2009, 73: 236-243.
[126] M. Levitt Protein folding by restrained energy minimization and molecular dynamics [J]. Journal of Molecular Biology. 1983, 170: 723-764.
[127] M. Levitt, M.F. Perutz. Aromatic rings act as hydrogen bond acceptors [J]. Journal of Molecular Biology. 1988, 201: 751-754.
[128] M. Hahn Receptor surface models. 1. definition and construction [J]. Journal of Medicinal Chemistry. 1995, 38: 2080-2090.
[129] R. Wang, Y. Fu, L. Lai. A new atom-additive method for calculating partition coefficients [J]. Journal of Chemical Information and Computer Science. 1997, 37: 615-621.
[130] A. Tadashi, M. Toshiyuki, Y. Ichiro. A New implicit solvent model for brownian dynamics simulation: solvent-accessible surface area dependent effective charge model [J]. Journal of Computer Chemistry. Japan. 2004, 3: 129-136.
[131] D.J. Abraham, G.E. Kellogg, J.M. Holt, G.K. Ackers. Hydropathic analysis of the noncovalentinteractions between molecular subunits of structurally characterized hemoglobins [J]. Journal of Molecular Biology. 1997, 272: 613-632.
[132] T.G. Gunnar, R.D. Umesh. Hydropathic interaction analyses of small organic activators binding to antithrombin [J]. Bioorganic & Medicinal Chemistry. 2004, 12: 633-640.
[133] G.E. Kellogg, D.G. Abraham. Hydrophobicity: is LogPo/w more than the sum of its parts [J]. European Journal of Medicinal. Chemistry. 2000, 35: 651-661.
[134] F.C. Wireko, G.E. Kellogg, D.J. Abraham. Allosteric modifiers of hemoglobin. crystallographically determined binding sites and hydrophobic binding/interaction analysis of novel hemoglobin oxygen effectors [J]. Journal of Medicinal Chemistry. 1991, 34: 758-767.
[135] G.E. Kellogg, G.S. Joshi, D.J. Abraham. New tools for modeling and understanding hydrophobicity and hydrophobic interactions [J]. Medicinal Chemistry Research. 1992, 1: 444-453.
[136] G.E. Kellogg, D.J. Abraham. KEY, LOCK, and LOCKSMITH. Complementary hydrophobicity map predictions of drug structure from a known receptor/receptor structure from known drugs [J]. Journal of Molecular Graphics. 1992, 10: 212-217.
[137] W. Hasel, T.F. Hendrikson, W.C. Still. A rapid approximation to the solvent accessible surface areas of atoms [J]. Tetrahed Comp. Method, 1988, 1: 103-116.
[138] J. Pei, Q. Wang, J. Zhou, L. Lai. Estimating protein-ligand binding free energy: atomic solvation parameters for partition coefficient and solvation free energy calculation [J]. Proteins. 2004, 57: 651-664.
[139] M. Pastor, G. Cruciani, I. McLay, S. Pickett, Sergio Clementi. Grid Independent Descriptors (GRIND) [J]. A Novel Class of Alignment-Independent Three-Dimensional Molecular Descriptors. Journal of Medicinal Chemistry. 2000, 43: 3233-3243.
[140] D. Riganelli, R. Valigi, G. Costantino, M. Baroni, S. Wold. Autocorrelation as a Tool for a Congruent Description of Molecules in 3D QSAR Studies [J]. Pharmaceutical Pharmacological Letters. 1993, 3: 5-8.
[141] F. Fontaine, M. Pastor, F. Sanz. Incorporating Molecular Shape into the Alignment-free GRid-INdependent Descriptors (GRIND) [J]. Journal of Medicinal Chemistry. 2004, 47: 2805-2815.
[142] P.J. Goodford. A computational procedure for determining energetically favourable binding sites on biologically important macromolecules [J]. Journal of Medicinal Chemistry. 1985, 28: 849-857.
[143] GRID v.22 Molecular Discovery Ltd., http://www.moldiscovery.com.
[144] R.D. Clark, E. Abrahamian, C. Abrams, P. Brandt, A.L. Gustavsson, E. Homan, N. Richmond,A. Strizhev, M. Wirstam, and P. Wolohan, Genetic Algorithm with Linear Assignment for Hypermolecular Alignment of Datasets [J]. manuscript in preparation.
[145] E. Abrahamian, P.C. Fox, L. N?rum, I. T. Christensen, H. Th?gersen, R.D. Clark. [J]. Journal of Chemical Information and Computer Science. 2003, 43: 458-468.
[146] V.J. Gillet, P. Willett, P.J. Fleming, D.V.S. Green. Designing focused libraries using MoSELECT [J]. Journal of Molecular Graphics and Modelling. 2002, 20: 491-498.
[147] S.J. Cottrell, V.J. Gillet, R. Taylor, D.J. Wilton. Generation of Multiple Pharmacophore Hypotheses using multiobjective optimisation techniques [J]. Journal of Computed-Aided Molecular Design. 2004: 18: 665-682.
[148] N.J. Richmond, P. Willett, R.D. Clark. Alignment of three-dimensional molecules using an image recognition algorithm [J]. Journal of Molecular Graphics and Modelling. 2004, 23: 199-209.
[149] G. Cruciani, M. Pastor, S. Clementi. Handling information from 3D grid maps for QSAR studies. Molecular Modeling and Prediction of Bioactivity. K. Gundertofte and F.E. J?rgensen (eds. Kluwer Academic/ Plenum Publishers [J]. New York. 2000, 73-82.
[150] G. Cruciani, P. Crivori, P.A. Carrupt, B. Testa. Molecular Fields in Quantitative Structure-Permeation Relationships: the VolSurf Approach [J]. Journal of Molecular Structure: THEOCHEM. 2000, 503: 17-30.
[151] P. Crivori, G. Cruciani, P.A. Carrupt, B. Testa. Predicting Blood-Brain Barrier Permeation from Three-Dimensional Molecular Structure [J]. Journal of Medicinal Chemistry. 2000, 43: 2204-2216.
[152] G. Cruciani, M. Meniconi, E. Carosati, I. Zamora, R. Mannhold. VOLSURF: a tool for drug ADME-properties prediction [J]. In Drug Bioavailability, Methods and Principles in Medicinal Chemistry. Edited by Van de Waterbeemd H, Lennerna¨s H, Artursson P. Weinheim: Wiley-VCH. 2003, 18: 406-419.
[153] T. I. Oprea, I. Zamora, A.L. Ungell. Pharmacokinetically Based Mapping Device for Chemical Space Navigation [J]. Journal of Combinatorial Chemisry. 2002; 4: 258-266.
[154] F. Lombardo, R.S. Obach, M.Y. Shalaeva, F. Gao. Prediction of Volume of Distribution Values in Humans for Neutral and Basic Drugs Using Physicochemical Measurements and Plasma Protein Binding Data [J]. Journal of Medicinal Chemistry. 2002, 45: 2867-2876.
[155] R. Pearlstein, R. Vaz; D. Rampe. Understanding the Structure?Activity Relationship of the Human Ether-a-go-go-Related Gene Cardiac K+ Channel. A Model for Bad Behavior [J]. Journal of Medicinal Chemistry. 2003, 46: 2017-2022.
[156] P. Crivori, I. Zamora, B. Speed, C. Orrenius, I. Poggesi. Model based on GRID-deriveddescriptors for estimating CYP3A4 enzyme stability of potential drug candidates [J]. Joural of Computer-Aided Molecular Design. 2004, 18: 155-166.
[157] D.B. Hibbert. Genetic algorithms in chemistry [J]. Chemometrics and Intelligent Laboratory Systems. 1993, 19: 277-293.
[158] J.H. Holland. Adaptation in natural and artificial systems [J]. Ann Arbor: University of Michigan Press. 1975: 156-159.
[159] D. Goldberg. Genetic algorithms in search, optimization, and machine learning [J]. New York: Addison-Wesley Publishing Company, Inc. 1989: 49-53.
[160] S.Wold, A.Ruhe, H. Wold. The collinearity problem in linear regression. The partial least squares approach to generalized inverses [J]. SIAM Journal of Scientific and Stattistical Computing. 1984, 5: 735-743.
[161] P. Nomikos, J.F. MacGregor. Multi-way partial least squares in monitoring batch processes [J]. Chemometrics and Intelligent Laboratory Systems. 1995, 30:97-108.
[162] R.J. Ergon. PLS score-loading correspondence and a biorthogonal factorization [J]. Chemometrics. 2002, 16: 368-373.
[163] S.D. Jong. SIMPLS: An alternative approach to partial least squares regression [J]. Chemometrics and Intelligent Laboratory Systems. 1993, 18: 251-263.
[164] J.T. Leonard, K. Roy. On selection of training and test sets for the development of predictive QSAR models [J]. QSAR & Combinatorial Science. 2006, 25: 235-251.
[165] G. A.Tropsha. Beware of q2 [J]. Journal of Molecular Graphics Modelling. 2002, 20: 269-276.
[166] P.Gramatica, P.Pilutti, E.Papa. Validated QSAR prediction of OH tropospheric degradation of VOCs: Splitting into training-test sets and consensus modeling [J]. Journal of Chemical Information Computer Science. 2004, 44:1794-1802.
[167] C. S?antai-Kis, I. K?vesdi, G. Kéri, L. ?rfi, Validation subset selections for extrapolation oriented QSPAR models [J]. Molecular Diversity. 2003, 7: 37-43.
[168] SYBYL'Leapforg Manual' version 7.3 [M]. Tripos, Inc., St. Louis, MO.
[169] D. Kuntz, J. M. Blaney, S. J. Oatley, R. Langridge, T. Ferrin. A Geometric Approach to Macromolecular-Ligand Interactions [J]. Journal of Molecular Biology. 1982, 161: 269-288.
[170] J. B. Moon, W. J. Howe. Computer Design of Bioactive Molecules: A Method for Receptor-Based de novo Ligand Design [J]. Proteins. 1991, 11: 314-328.
[171] V. Gillet, A.P. Johnson, P. Mata, S. Sike, P. Williams. SPROUT: A Program for Structure Generation [J]. Tet. Computer Methods. 1992, 1-34.
[172] H. J. Boehm. The Computer Program LUDI: A New Method for de novo Design of Enzyme Inhibitors [J]. Journal of Computer-Aided Molcular Design. 1992, 6: 61-78.
[173] W. R. Payne, R.C. Glen. Molecular Recognition Using a Binary Genetic Search Algorithm [J]. Journal of Molecular Graphics. 1993, 11: 74.
[174] Y.C. Martin. 3D Database Searching in Drug Design [J]. Journal of Medicinal Chemistry. 1992, 35: 2145-2154.
[175] D.J. Danziger, P.M. Dean. Automated Site-Directed Drug Design: The Prediction and Observation of Ligand Point Positions at Hydrogen-Bonding Positions on Proteins Surfaces [J]. Proceedings the Roral of Society. 1989, B236: 115-124.
[176] Miranker, M. Karplus. Functionality maps of binding sites: a multiple copy simultaneous search method [J]. Proteins, 1991, 11: 29-34.
[177] Y. Nishibata, A. Atai. Automatic Creation of Drug Candidate Structures Based on Receptor Structure. Starting Point for Artificial Lead Generation [J].Tetrahedron. 1991, 47: 8985.
[178] C.M.W. Ho, G.R. Marshall. FOUNDATION: A program to retrieve all possible structures containing a suer-defined minimum number of matching query elements from three-dimensional databases [J]. Journal of Computer-Aided Molcular Design. 1993, 7: 3-23.
[179] D. S. Goodsell, A. J. Olson. Automated Docking of Substrates to Proteins by Simulated Annealing [J]. Proteins 1990. 8: 195-202.
[180] M. C. Lawrence, P.C. Davis. CLIX: A Search-Algorithm for Finding Novel Ligands Capable of Binding Proteins of Known Three-Dimensional Structure [J]. Proteins. 1992, 12: 31-41.
[181] C.L.M.J. Verlinde, G. Rudenko, W. G. Hol. In Search of New Lead Compounds for Trypanosmiasis: A Protein Structure-Based Linked-Fragment Approach [J]. Journal of Computer-Aided Molcular Design. 1992, 6: 131-147.
[182] J. Singh, J. Saldanha, J.M. Thornton, A Novel Method for the Modeling of Peptide Ligands to their Receptors [J]. Protein Engineering. 1991, 4: 251-261.
[183] S.H. Rotstein, M.A. Murcko. GenStar 1.0: A Method for de novo Drug Design [J]. Journal of Computer-Aided Molcular Design. 1993, 7: 23-43.
[184] R.A. Lewis, D.C. Roe, C. Huang, T.C. Ferrin, R. Langridge, I.D. Kuntz. Automated. Site-Directed Drug Design Using Molecular Lattices [J]. Journal of Molecular Graphics and modeling. 1992, 10: 66-67.
[185] D.J. Bacon, J.Moult. Docking by Least-Squares Fitting of Molecular Surface Patterns [J]. Journal of Molecular Biology. 1992, 225: 849-858.
[186] S. Dixon, J. Blaney, D. Weininger. Presented at the Third York Meeting—Characterizing and Satisfying the Steric and Chemical Restraints of Binding Sites [J]. 1993, March: 29-30.
[187] R. D. Cramer III, D. E. Patterson, J. D. Bunce. Comparative Molecular Field Analysis (CoMFA). Effect of Shape on Binding of Steroids to Carrier Proteins [J]. Journal of theAmerican Chemistry Society. 1988, 110: 5959-5967.
[188] M. Clark, R. D. Cramer III, N. Van Opdenbosch. Validation of the General Purpose TRIPOS 5.2 Force Field [J]. Journal of Computational Chemistry. 1989, 10: 982-1012.
[189] R.S. Bohacek, C. McMartin. Definition and Display of Steric, Hydrophobic, and Hydrogen-Bonding Properties of Ligand Binding Sites in Proteins Using Lee and Richards Accessible Surfaces: Validation of a High-Resolution Tool for Drug Design [J]. Journal of Medicinal Chemistry. 1992, 35: 1671-1684.
[190] P. J. Goodford. A Computational Procedure for Determining Energetically Favorable Binding Sites on Biologically Important Macromolecules [J]. Journal of Medicinal Chemistry. 1985, 28: 849-857.
[191] N. Pattabiraman, M. Levitt, T. E. Ferrin, R. Langridge. Computer Graphics in Real-Time Docking with Energy Calculation and Minimization [J]. Journal of Computational Chemistry. 1985, 6: 432-436.
[192] P.C. Nair, E.M. Sobhia. CoMFA based de novo design of pyridazine analogs as PTP1B inhibitors [J]. Journal of Molecular Graphics and Modeling. 2007, 26: 117-123.
[193] A. Kumar, M.I. Siddiqi. CoMFA based de novo design of Pyrrolidine Carboxamides as Inhibitors of Enoyl Acyl Carrier Protein Reductase from Mycobacterium tuberculosis [J]. Journal of Molecular Modeling. 2008, 14: 923-935.
[194] S. Forrest. Genetic Algorithm: Principles of Natural Selection Applied to Computation [J]. Science, 1993, 261: 872-878.
[195] R. Dawkins. The Selfish Gene [J]. Oxford University Press, Oxford, UK, 1976.
[196] Y. C. Martin. 3D database searching in drug design. [J]. Journal of Medicianl Chemistry 1992, 35: 2145.-2154.
[197] S.M. Wang, D.W. Zaharevitz, R. Sharma, V.E. Marquez, N.E. Lewin, L. Du, P.M. Blumberg, G.W.A. Milne. The Discovery of Novel, Structurally Diverse Protein Kinase C Agonists through Computer 3D-Database Pharmacophore Search. Molecular Modeling Studies [J]. Journal of Medicianl Chemistry. 1994, 37: 4479-4489.
[198] Y. Kurogi, O. F. Guner. Pharmacophore modeling and three-dimensional database searching for drug design using catalyst [J]. Current Medicinal Chemistry. 2001, 8: 1035-1055.
[199] S. Chun, J.Y. Lee, S.G. Ro, K.W. Jeong, Y. Kim, C.J. Yoon. 3D Quantitative and Qualitative Structure-Activity Relationships of theα-Opioid Receptor [J]. Antagonists Bulletin of the Korean Chemical Society. 2008, 29: 656-662.
[200] K.C. Tsai, L.W. Teng, Y.M. Shao, Y.C. Chen, Y.C. Lee, M. Li, N.W. Hsiao. The first pharmacophore model for potent NF-kB inhibitors [J]. Bioorganic & Medicinal ChemistryLetters. 2009, 19: 5665-5669.
[201] Sybyl 7.0 manual [M]. Tripos Inc., 1699 South Hanley Rd, St. Louis, MO, U.S.A. 2004.
[202] Y.C. Martin. C.Hansch, T.E. Fujita, D.C. Washington: In classical and 3D QSAR in agrochemistry [J]. American Chemical Society. 1995: 318-329.
[203] G. Jones, P. Willett, R.C. Glen. A genetic algorithm for flexible molecular overlay and pharmacophore elucidation [J]. Journal of Computer-Aided Molecular Design. 1995, 9: 532-549.
[204] T. Langer, G. Wolber. Pharmacophore definition and 3D searches [J]. Drug Discovery Today: Technology. 2004, 1: 203-207.
[205] G.M. Rishton. Reactive compounds and in vitro false positive in HTS [J]. Drug Discovery Today. 1997, 2: 382-384.
[206] C.A. Lipinski, F. Lombardo, B.W. Dominy. et al. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings [J]. Advance Drug Delivery Reviews. 2001, 46: 3-26.
[207] N.R. Taylor, A. Cleasby, O. Singh, T. Skarzynski, A.J. Wonacott, P.W. Smith, S.L. Sollis, P.D. Howes, P.C. Cherry, R. Bethell, P. Colman, J. Varghese. Dihydropyrancarboxamides related to zanamivir: a new series of inhibitors of influenza virus sialidases. 2. Crystallographic and molecular modeling study of complexes of 4-amino-4H-pyran-6-carboxamides and sialidase from influenza virus types A and B [J]. Journal of Medicinal Chemistry. 1998, 41: 798-807.
[208] B.J. Smith, J.L. McKimm-Breshkin, M. McDonald, R.T. Fernley, J.N. Varghese, P.M. Colman. Structural studies of the resistance of influenza virus neuramindase to inhibitors [J]. Journal of Medicinal Chemistry. 2002, 45: 2207-2212.
[209] J.N. Varghese, V.C. Epa, P.M. Colman. Three-dimensional structure of the complex of 4-guanidino-Neu5Ac2en and influenza virus neuraminidase [J]. Protein Science. 1995, 4: 1081-1087.
[210] G.T. Wang, S. Wang, R. Gentles, T. Sowin, C.J. Maring, D.J. Kempf, W.M. Kati, V. Stoll, K.D. Stewart, G. Laver. Design, synthesis, and structural analysis of inhibitors of influenza neuraminidase containing a 2,3-disubstituted tetrahydrofuran-5-carboxylic acid core [J]. Bioorganic & Medicinal Chemistry Letters. 2005, 15: 125-128.
[211] R.J. Russell, L.F. Haire, D.J. Stevens, P.J. Collins, Y.P. Lin, G.M. Blackburn, A.J. Hay, S.J. Gamblin, J.J. Skehel. The structure of H5N1 avian influenza neuraminidase suggests new opportunities for drug design [J]. Nature. 2006, 443: 45-49.
[212] J.N. Varghese, P.W. Smith, S.L. Sollis, T.J. Blick. A. Sahasrabudhe. J.L. McKimm-Breschkin. P.M. Colman. Drug design against a shifting target: a structural basis for resistance toinhibitors in a variant of influenza virus neuraminidase [J]. Structure. 1998, 6: 735-746.
[213] X. Xu, X. Zhu, R.A. Dwek, J. Stevens, I.A. Wilson. Structural characterization of the 1918 influenza virus H1N1 neuraminidase [J]. the Journal of Virology. 2008, 82: 10493-10501.
[214] G.T. Wang, Y.W. Chen, S. Wang, R. Gentles, T. Sowin, W. Kati, S. Muchmore, V. Giranda, K.t Stewart, H. Sham, D. Kempf, W. Graeme Laver. Design, Synthesis, and Structural Analysis of Influenza Neuraminidase Inhibitors Containing Pyrrolidine Cores [J]. Journal of Medicinal Chemistry. 2001, 44:1192-1201.
[215] J. Zhang, Q. Wang, H. Fang, W.F. Xu, A.L. Liu, G.H. Du. Design, synthesis, inhibitory activity, and SAR studies of pyrrolidine derivatives as neuraminidase inhibitors [J]. Bioorganic & Medicinal Chemistry. 2007, 15: 2749-2758.
[216] C.J. Maring, V.S. Stoll, C. Zhao, M.H. Sun, A.C. Krueger, K.D. Stewart, D.L. Madigan, W.M. Kati, Y.B. Xu, R.J. Carrick, D.A. Montgomery, A. Kempf-Grote, K.C. Marsh, A. Molla, K.R. Steffy, H.L. Sham, W. Graeme Laver, Y.G. Gu, D.J. Kempf, W.E. Kohlbrenner, Structure-Based Characterization and Optimization of Novel Hydrophobic Binding Interactions in a Series of Pyrrolidine Influenza Neuraminidase Inhibitors [J]. Journal of Medicinal Chemistry. 2005, 48: 3980-3990.
[217] A.C. Krueger, Y. Xu, W.M. Kati, D.J. Kempf, C.J. Maring, K.F. McDaniel, A. Molla, D. Montgomery, W.E. Kohlbrenner. Synthesis of potent pyrrolidine influenza neuraminidase inhibitors [J]. Bioorganic & Medicinal Chemistry Letters. 2008, 18:1692-1695.
[218] A.M.A. Hammad, F.U. Afifi, M.O. Taha, Combining docking, scoring and molecular field analyses to probe influenza neuraminidase–ligand interactions [J]. Journal of Molecular Graphics and Modelling. 2007, 26: 443-456.
[219] I.D. Wall, A.R. Leach, D.W. Salt, M.G. Ford. J.W. Essex. Binding Constants of Neuraminidase Inhibitors: An Investigation of the Linear Interaction Energy Method [J]. Journal of Medicinal Chemistry. 1999, 42: 5142-5152.
[220] Y. Cheng, W. Prusoff. Relationship between the inhibition constant (Ki and the concentration of inhibitor which causes 50 percent inhibition (I50 of an enzymatic reaction [J]. Biochemical Pharmacology. 1973, 22: 3099-3108.
[221] M.Y. Zheng, K.Q. Yu, H. Liu, X.M. Luo, K.X. Chen , W.L. Zhu and H.L. Jiang, QSAR analyses on avian influenza virus neuraminidase inhibitors using CoMFA, CoMSIA, and HQSAR [J]. Journal of Computer-Aided Molecular Design. 2006, 20: 549-566.
[222] X.Yi, Z.R. Guo, F.M. Chu. Study on Molecular Mechanism and 3D-QSAR of Influenza Neuraminidase Inhibitors [J]. Bioorganic & Medicinal Chemistry. 2003, 11: 1465-1474.
[223] S. Renner, S. Derksen, S. Radestock, F. M?rchen. Maximum Common Binding Modes(MCBM: Consensus Docking Scoring Using Multiple Ligand Information and Interaction Fingerprints [J]. Journal of Chemical Information and Modeling. 2008, 48: 324.
[224] R.D. Clark, P.C. Fox. Statistical variation in progressive scrambling [J]. Journal of Computer-Aided Molecular Design. 2004, 18: 563–576.
[225] C.W. Sun, X.D. Zhang, H. Huang, P. Zhou. Synthesis and evaluation of a new series of substituted acyl(thio)urea and thiadiazolo [2,3-a] pyrimidine derivatives as potent inhibitors of influenza virus neuraminidase [J]. Bioorganic & Medicinal Chemistry. 2006, 14: 8574-8581.
[226] J. Sun, S. Cai, J. Li, N. Yan, Y. Wang, H. Mei. Docking and 3D QSAR study of thiourea analogues as potent inhibitors of influenza virus neuraminidase [J]. Journal of Molecular Modeling. DOI: 10.1007/s00894-010-0685-9.
[227] M. Clark, R.D. Crame. The probability of chance correlation using partial least squares (PLS) [J]. Quantitative Structure-Activity Relationships. 1993, 2: 137-145.
[228] S.W. Yao, V.H.C. Lopes, F. Fernández, X. García-Mera, M. Morales, J.E. Rodríguez-Borges, M.N.D.S. Cordeiro. Synthesis and QSAR study of the anticancer activity of some novel indane carbocyclic nucleosides [J]. Bioorganic & Medicinal Chemistry. 2003, 11: 4999-5006.
[229] A.L. Liu, H.D. Wang, S.M.Y. Lee, Y.T. Wang, G.H. Du. Structure-activity relationship of flavonoids as influenza virus neuraminidase inhibitors and their in vitro anti-viral activities [J]. Bioorganic & Medicinal Chemistry. 2008, 16: 7141-7147.
[230] K. Miki, T.Nagai, K. Suzuki, R. Tsujimura, K. Koyama, K. Kinoshita, K. Furuhata, H. Yamada, K. Takahashi. Anti-influenza virus activity of biflavonoids [J]. Bioorganic & Medicinal Chemistry Letters. 2007, 17: 772-775.
[231] Y.B. Ryu, M.J. Curtis-Long, J.H. Kim, S.H. Jeong, M.S. Yang, K.W. Lee, W.S. Lee, K.H. Park. Pterocarpans and flavanones from Sophora flavescens displaying potent neuraminidase inhibition [J]. Bioorganic & Medicinal Chemistry Letters. 2008, 18: 6046-6049.
[232] Y.B. Ryu, M.J. Curtis-Long, J.W. Lee, J.H. Kim, J.Y. Kim, K.Y. Kang, W.S. Lee, K.H. Park. Characteristic of neuraminidase inhibitory xanthones from Cudrania tricuspidata [J]. Bioorganic & Medicinal Chemistry. 2009, 17: 2744-2750.
[233] W. Lew, H. Wu, X. Chen, B.J. Graves, P.A. Escarpe, H.L. MacArthur, D.B. Mendel, C.U. Kim. Carbocyclic Influenza Neuraminidase Inhibitors Possessing a C3-Cyclic Amine Side Chain: Synthesis and Inhibitory Activity [J]. Bioorganic & Medicinal Chemistry Letters. 2000, 10: 1257-1260.
[234] W. Lew, H. Wu, D.B. Mendel, P.A. Escarpe, X. Chen, W.G. Laver, B.J. Graves, C.U. Kim. A new series of c3-aza carbocyclic influenza neuraminidase inhibitors: synthesis and inhibitoryactivityl [J]. Bioorganic & Medicinal Chemistry Letters.1998, 8: 3321-3324.
[235] C.U. Kim, W. Lew, M.A. Williams, H. Liu, L. Zhang, S. Swaminathan, N. Bischofberger, M.S. Chen, D.B. Mendel, C.Y. Tai, W.G. Laver, R.C. Stevens. Influenza Neuraminidase Inhibitors Possessing a Novel Hydrophobic Interaction in the Enzyme Active Site: Design, Synthesis, and Structural Analysis of Carbocyclic Sialic Acid Analogues with Potent Anti-Influenza Activity [J]. Journal of the American Chemistry Society. 1997, 119: 681-690.
[236] C.U. Kim, W. Lew, M.A. Williams, H. Wu, L. Zhang, X. Chen, P.A. Escarpe, D.B. Mendel, W. G. Laver, R.C. Stevens. Structure-Activity Relationship Studies of Novel Carbocyclic Influenza Neuraminidase Inhibitors [J]. Journal of Medicinal Chemistry. 1998, 41: 2451-2460.
[237] M.A. Williams, W. Lew, D.B. Mendel, C.Y. Tai, P.A. Escarpe, W.G. Laver, R.C. Stevens, C.U. Kim. Structure-activity relationships of carbocyclic influenza neuraminidase inhibitors [J]. Bioorganic & Medicinal Chemistry Letters. 1997, 7: 1837-1842.
[238] P. Chand, P.L. Kotian, P.E. Morris, S. Bantia, D.A. Walsh, Y.S. Babua. Synthesis and inhibitory activity of benzoic acid and pyridine derivatives on influenza neuraminidase [J]. Bioorganic & Medicinal Chemistry. 2005, 13: 2665-2678.
[239] J. Zhang, Q. Wang, H. Fang, W. Xu, A. Liu, G. Du. Design, synthesis, inhibitory activity, and SAR studies of hydrophobic p-aminosalicylic acid derivatives as neuraminidase inhibitors [J]. Bioorganic & Medicinal Chemistry. 2008, 16: 3839-3847.
[240] P.D. Howes, A. Cleasby, D.N. Evans, H. Feilden, P.W. Smith, S.L. Sollis, N. Taylor, A.J. Wonacott. 4-acetylamino-3-(imidazol-1-yl-benzoic acids as novel inhibitors of influenza sialidase [J]. European journal of medicinal chemistry. 1999, 34: 225-234.
[241] V.R. Atigadda, W.J. Brouillette, F. Duarte, Y.S. Babu, S. Bantia, P. Chand, N. Chu, J.A. Montgomery, D.A. Walsh, E. Sudbeck, J. Finley, G.M. Air, M. Luo, G.W. Laver. Hydrophobic Benzoic Acids as Inhibitors of Influenza Neuraminidase [J]. Bioorganic & Medicinal Chemistry. 1999, 7: 2487-2497.
[242] V.R. Atigadda, W.J. Brouillette, Franco Duarte, Shoukath M. Ali, Yarlagadda S. Babu, Shanta Bantia, Pooran Chand, Naiming Chu, John A. Montgomery, David A. Walsh, Elise A. Sudbeck, James Finley, Ming Luo, Gillian M. Air, Graeme W. Laver. Potent Inhibition of Influenza Sialidase by a Benzoic Acid Containing a 2-Pyrrolidinone Substituent [J]. Journal of Medicinal Chemistry. 1999, 42, 2332-2343.
[243] P. Chand, Y.S. Babu, S. Bantia, N. Chu, L. B. Cole, P.L. Kotian, W. Graeme Laver, J.A. Montgomery, V.P. Pathak, S.L. Petty, D.P. Shrout, D.A. Walsh, G.M. Walsh. Design and Synthesis of Benzoic Acid Derivatives as Influenza Neuraminidase Inhibitors UsingStructure-Based Drug Design1 [J]. Journal of Medicinal Chemistry. 1997, 40: 4030-4052.
[244] P. Chand, Y.S. Babu, S. Bantia, S. Rowland, A. Dehghani, P.L. Kotian, T.L. Hutchison, S. Ali, W. Brouillette, Y. El-Kattan, T. H. Lin. Syntheses and Neuraminidase Inhibitory Activity of Multisubstituted Cyclopentane Amide Derivatives [J]. Journal of Medicinal Chemistry. 2004, 47: 1919-1929.
[245] J.Y. Sun, S.X. Cai, N. Yan, H. Mei. Docking and QSAR studies of influenza neuraminidase inhibitors using three-dimensional holographic vector of atomic interaction field analysis [J]. European Journal of Medicinal Chemistry. 2010, 45: 1008-1014.
[2] J.L. McKimm-Breschkin. Resistance of influenza viruses to neuraminidase inhibitors—a review [J]. Antiviral Research. 2000, 47: 1-17.
[3] L.V. Gubareva, L. Kaiser, F.G. Hayden. Influenza virus neuraminidase inhibitors [J]. Lancet 2000, 355: 827-835.
[4] O. Ferraris, B. Lina. Mutations of neuraminidase implicated in neuraminidase inhibitors resistance [J]. Journal of Clinical Virology. 2008, 41: 13-19.
[5] World Health Organization (WHO), Bull [J]. WHO. 1980, 58: 585.
[6] W.J. Brouillette, S.N. Bajpai, S.M. Ali, S.E. Velu, V.R. Atigadda, B.S. Lommer, J.B. Finley, M. Luo, G.M. Air. Pyrrolidinobenzoic Acid Inhibitors of Influenza Virus Neuraminidase: Modifications of Essential Pyrrolidinone Ring Substituents [J]. Bioorganic & Medicinal Chemistry. 2003, 11: 2739-2749.
[7] M. M. Mukhtar, S. T. Rasool, D. Song, C. Zhu, Q. Hao, Y. Zhu, J. Wu. Origin of highly pathogenic H5N1 avian influenza virus in China and genetic characterization of donor and recipient viruses [J]. Journal of General Virology Virol. 2007, 88: 3094-3099.
[8] N. Shirvan, M. Moradi, M. Aminian, R. Madani. Preparation of Neuraminidase-Specific Antiserum from the H9N2 Subtype of Avian Influenza Virus [J]. Turkish Journal of Veterinary andAnimal Science. 2007, 31: 219-223.
[9] Q. Zhang, J. Yang, K. Liang, L. Feng, S. Li, J. Wan, X. Xu, G. Yang, D. Liu, S. Yang. Binding interaction analysis of the active site and its inhibitors for neuraminidase (N1 subtype) of human influenza virus by the integration of molecular docking, FMO calculation and 3D-QSAR CoMFA modeling [J]. Journal of Chemical Information and Modeling. 2008, 48: 1802-1812.
[10] S.U. Schnitzler, P. Schnitzler. An update on swine-origin influenza virus A/H1N1: a review [J]. Virus Genes. 2009, 39: 279-292.
[11] Q.S. Du, S.Q. Wang, R.B. Huang, K.C. Chou. Computational 3D structures of drug-targeting proteins in the 2009-H1N1 influenza a virus [J]. Chemical Physics Letters. 2010, 485: 191-195.
[12] Centers for Disease Control and Prevention. Prevention and control of influenza: recommendations of the Advisory Committee on Immunization Practices (ACIP. MMWR:Morbidity and Mortality Weekly Report [J] 1999, 48: 1-28.
[13] T. Betakova. M2 Protein–A Proton Channel of Influenza A Virus [J]. Current Pharmaceutical Design. 2007, 13: 3231-3235.
[14] G.J. Galasso. An assessment of antiviral drugs for the management of infectious diseases in human [J]. Antiviral Research. 1981, 1: 73-96.
[15] A.B. Tuzikov, N.E. Byramova, N.V. Bovin, A.S. Gambaryan, M.N. Matrosovich. Monovalent and polymeric 5N-thioacetamido sialosides as tightly-bound receptor analogs of influenza viruses [J]. Antiviral Research. 1997, 33: 129-134.
[16] T. Hatta, K. Takai, S. Nakada, T. Yokota, H. Takaku. Specific inhibition of influenza virus RNA polymerase and nucleoprotein genes expression by liposomally endocapsulated antisense phosphorothioate oligonucleotides in clone 76 cells [J]. Biochemical and Biophysical Research Communications. 1997, 232: 545-549.
[17] R.F. Service Researchers seek new weapon against the flu [J]. Science. 1997, 275: 756-757.
[18] J.S. Oxford, P. Novelli , A. Sefton, R. Lambkin. New millennium antivirals against pandemic and epidemic influenza: the neuraminidase inhibitors [J]. Antivir Chemistry & Chemotherapy. 2002, 13: 205-217.
[19] D.P. Calfee, F.G. Hayden. New approaches to influenza chemotherapy neuraminidase inhibitors [J]. Drugs. 1998, 56: 537-553.
[20] W.G. Laver, N. Bischofberger, R.G. Webster. Disarming flu viruses [J]. Scientific American. 1999, 280: 78-82.
[21] P.J. Gavin, R.B. Thomson. Review of rapid diagnostic tests for influenza [J]. Clinical and Applied Immunology Reviews. 2003, 4: 151-172.
[22] S. Schultz-Cherry, V.S. Hinshaw. Influenza virus neuraminidase activates latent transforming growth factor beta [J]. Journal of Virology. 1996, 70: 8624-8629.
[23] M.N. Janakiraman, C.L. White, W.G. Laver, G.M. Air, M. Luo. Structure of influenza virus neuraminidase B/Lee/40 complexed with sialic acid and a dehydro analogue at 1.8? resolution: implication for the catalytic mechanism [J]. Biochemistry. 1994, 33: 8172-8179.
[24] J.N. Varghese, J.L. Mckimm-Breschkin, J.B. Caldwell, A.A. Kortt, P.M. Colman. The structure of the complex between influenza virus neuraminidase and sialic acid, the viral receptor [J]. Proteins: Structure, Function, and Genetics. 1992, 14: 327-332.
[25] P. Bossart-Whitaker, M. Carson, Y.S. Babu, C.D. Smith, W.G. Laver, G.M. Air. Threedimensional structure of influenza A N9 neuraminidase and its complex with the inhibitor 2-deoxy-2,3-dehydro-N-acetyl neuraminic acid [J]. Journal of Molecular Biology. 1993, 232: 1069-1083.
[26] N.R. Taylor, A. Cleasby, O. Singh, T. Skarzynski, A.J. Wonacott, P.W. Smith, S.L. Sollis, P.D. Howes, P.C. Cherry, R. Bethell, P. Colman, J. Varghese. Dihydropyrancarbox-amides related to zanamivir: a new series of inhibitors of influ-enza virus sialidases. 2. crystallographic and molecular modeling study of complexes of 4-amino-4H-pyran-6-carboxamides and sialidase from influenza virus types A and B [J]. Journal of Medicianl Chemistry. 1998, 41: 798-807.
[27] D. Low. Reducing antibiotic use in influenza: challenges and rewards. Clinical Microbiology and Infection [J]. 2008, 14: 298-306.
[28] T. Wang, R.C. Wade. Comparative Binding Energy (COMBINE) Analysis of Influenza Neuraminidase-Inhibitor Complexes [J]. Journal of Medicinal Chemistry. 2001, 44: 961-971.
[29] A.U. Khan, S. Shakil, S.K. Lal. Effi cacy of neuraminidase (NA) inhibitors against H1N1 strains of different geographical regions: an in silico approach [J]. Indian Journal of Microbiology. 2009, 49: 370-376.
[30] J. Zhang, K. Yu, W. Zhua, H. Jiang. Neuraminidase pharmacophore model derived from diverse classes of inhibitors [J]. Bioorganic & Medicinal Chemistry Letters. 2006, 16: 3009-3014.
[31] A.M.A. Hammad, M.O. Taha. Pharmacophore Modeling, Quantitative Structure-Activity Relationship Analysis, and Shape-Complemented in Silico Screening Allow Access to Novel Influenza Neuraminidase Inhibitors [J]. Journal of Chemical Information and Modeling. 2009, 49: 978-996.
[32] A.T. García-Sosa, S. Sild, U. Maran. Docking and Virtual Screening Using Distributed Grid Technology [J]. QSAR Combinatorial Science. 28, 2009, 8: 815-821.
[33] X. Yi, Z. Guo, F.M. Chu. Study on Molecular Mechanism and 3D-QSAR of Influenza Neuraminidase Inhibitors [J]. Bioorganic & Medicinal Chemistry. 2003, 11: 1465-1474.
[34] R.P. Verma, C.A. Hansch. QSAR study on influenza neuraminidase inhibitors [J]. Bioorganic & Medicinal Chemistry. 2006, 14: 982-996.
[35] P.C. Nair, M.E. Sobhia. Quantitative structure activity relationship studies on thiourea analogues as influenza virus neuraminidase inhibitors [J]. European Journal of Medicinal Chemistry. 2008, 43: 293-299.
[36] Q.S. Du, R.B. Huang, Y.T. Wei, L.Q. Du, K.C. Chou. Multiple Field Three Dimensional Quantitative Structure-Activity Relationship (MF-3D-QSAR) [J]. Journal of Computational Chemistry. 2008, 29: 211-219.
[37] W.J. Lü, Y.L. Chen, W.P. Ma, X.Y. Zhang, F. Luan, M.C. Liu, X.G. Chen, Z.D. Hu. QSAR study of neuraminidase inhibitors based on heuristic method and radial basis function network [J]. European Journal of Medicinal Chemistry. 2008, 43: 569-576.
[38] Q.S. Du, R.B. Huang, Y.T. Wei, Z.W. Pang, L.Q. Du, K.C. Chou. Fragment-Based Quantitative Structure–Activity Relationship (FB-QSAR) for Fragment-Based Drug Design [J]. Journal of Computational Chemistry. 2009, 30: 295-304.
[39] W.M. Kati, D. Montgomery, C. Maring, V.S. Stoll, V. Giranda, X. Chen, W. Graeme Laver, W. Kohlbrenner, D.W. Norbeck. Novelα- andβ-Amino Acid Inhibitors of Influenza Virus Neuraminidase [J]. Antimicrobial Agents and Chemotherapy. 2001, 45: 2563-2570.
[40] J. Zhang, W. Xu, A. Liu, G. Du. Design, Synthesis, and Preliminary Evaluation of New Pyrrolidine Derivatives as Neuraminidase Inhibitors [J]. Medicinal Chemistry. 2008, 4, 206-209.
[41] V. Stoll, K.D. Stewart, C.J. Maring, S. Muchmore, V. Giranda, Y.Y. Gu, G. Wang, Y. Chen, M. Sun, C. Zhao, A.L. Kennedy, D.L. Madigan, Y. Xu, A. Saldivar, W. Kati, G. Laver, T. Sowin, H.L. Sham, J. Greer, D. Kempf. Influenza Neuraminidase Inhibitors: Structure-Based Design of a Novel Inhibitor Series [J]. Biochemistry. 2003, 42, 718-727.
[42] Y. Sudhakar Babu, P. Chand, S. Bantia, P. Kotian, A. Dehghani, Y. El-Kattan, T.H. Lin, T. L. Hutchison, A. J. Elliott, C. D. Parker, S.L. Ananth, L.L. Horn, G.W. Laver, J.A. Montgomery. BCX-1812 (RWJ-270201): Discovery of a Novel, Highly Potent, Orally Active, and Selective Influenza Neuraminidase Inhibitor through Structure-Based Drug Design [J]. Journal of Medicianl Chemistry. 2000, 43: 3482-3486.
[43] P. Chand, P.L. Kotian, A. Dehghani, Y. El-Kattan, T.H. Lin, T.L. Hutchison, Y. Sudhakar Babu, S. Bantia, A.J. Elliott, J.A. Montgomery. Systematic Structure-Based Design and Stereoselective Synthesis of Novel Multisubstituted Cyclopentane Derivatives with Potent Antiinfluenza Activity [J]. Journal of Medicianl Chemistry. 2001, 44: 4379-4392.
[44] K. Miki, T. Nagai, K. Suzuki, R. Tsujimura, K. Koyama, K. Kinoshita, K. Furuhata, H. Yamada, K. Takahashi. Anti-influenza virus activity of biflavonoids [J]. Bioorganic & Medicinal Chemistry Letters. 2007, 17: 772-775.
[45] J.J. Shie, J.M. Fang, S.Y. Wang, K.C. Tsai, Y.S.E. Cheng, A.S. Yang, S.C. Hsiao, C.Y. Su, C.H. Wong. Synthesis of Tamiflu and its Phosphonate Congeners Possessing Potent Anti-Influenza Activity [J]. Journal of the American Chemical Society. 2007, 129: 11892-11893.
[46] M. Hochgürtel, R. Biesinger, H. Kroth, D. Piecha, M.W. Hofmann, S. Krause, O. Schaaf, C. Nicolau, A.V. Eliseev. Ketones as Building Blocks for Dynamic Combinatorial Libraries: Highly Active Neuraminidase Inhibitors Generated via Selection Pressure of the Biological Target [J]. Journal of Medicianl Chemistry. 2003, 46: 356-358.
[47] A.M.A. Hammad, F.U. Afifi, M.O. Taha. Combining docking, scoring and molecular field analyses to probe influenza neuraminidase–ligand interactions [J]. Journal of MolecularGraphics and Modelling. 2007, 26: 443-456.
[48] C. Sangma, S. Hannongbua. Structural information and computational methods used in design of neuraminidase inhibitors [J]. Current Computer-Aided Drug Design. 2007, 3: 113-132.
[49] C. Hansch, R.M. Muir, T. Fujita, P.P. Maloney, F. Geiger, M. Streich. The Correlation of biological activity of plant growth regulators and chloromycetin derivatives with hammett constants and partition coefficients [J]. Journal of the American Chemical Society. 1963, 85: 2817.
[50] C.Hansch, T.Fujita. F-σ-πanalysis. a method for the correlation of biological activity and chemical structure [J]. Journal of the American Chemical Society. 1964, 86: 1616.
[51] I.B. Bersuker, A.S. Dimoglo, M.Y. Gorbachov. The Electron-topological approach in investigation of structure-activity-relation-inhibition of thymidine phosphorylase by uracil derivatives [J]. Bioorgan Khim. 1987, 13:38.
[52] I.B. Bersuker, A.S. Dimoglo, M.Y. Gorbachov, P.P. Maloney. Origin of musk fragrance activity-the electron-topologic approach [J]. New Journal of Chemistry. 1991, 15: 307.
[53] S.H. Free, J.W. Wilson. A mathematical contribution to structure-activity studies [J]. Journal of Medicianl Chemistry. 1964, 7: 395.
[54] T. Aoyama, Y. Suzuki, H. Ichikawa. Neural nerworks applied to pharmaceutical problems. 1. method and application to decision making [J]. Chemical & Pharmaceutical Bulletin. 1989, 37: 2558.
[55] G.M. Crippen. Distance geometry approach to rationalizing binding date [J]. Journal of Medicianl Chemistry. 1979, 22: 988.
[56] G.M. Crippen. Quantitative structure-activity-ralationships by distance geometry-systematic analysis of dihydrofolate-reductase inhibitors [J]. Journal of Medicianl Chemistry. 1980, 23: 599.
[57] G.M.Crippen. Quantitative structure-activity-relationships by distance geometry- thyroxine binding-site [J]. Journal of Medicianl Chemistry. 1981, 24: 198.
[58] L.G. Boulu, G.M. Crippen, H.A. Barton, H. Kwon. M.A. Marletta. Voronoi binding-site model of a polycyclic aromatic hydrocarbon binding-protein [J]. Journal of Medicianl Chemistry. 1990, 33:771.
[59] A.J. Hopfiger. A QSAR investigation of dihydrofolate-reductase inhibition by baker triazines based upon molecular shape analysis [J]. Journal of the American Chemical Society. 1980, 102:7196.
[60] A.J. Hopfiger, R. Potenzone. Ames test and anti-tumor activity of 1-(X-phenyl)-3, 3-dialkyltriazenes-quantitative stucture activity studies based upon molecular sharp analysis[J]. Molecular Pharmacology. 1982, 21: 187.
[61] R.D. Cramer III, D.E. Patterson, J.D. Bunce. Comparative molecular field analysis (CoMFA. 1. Effect of shape on binding of steroids to carrier proteins [J]. Journal of the Ameican Chemical Society. 1988, 110: 5959-5967.
[62] A.N. Jain, K. Koile, D. Chapman. COMPASS-predicting biological-activities from molecular-surface properties-performance comparisons on a steroid benchmark [J]. Journal of Medicianl Chemistry. 1994, 37:2315.
[63] A.N. Jain, T.G. Dietterich, R.H. Lathrop, D. Chapman. COMPASS-a sharp-based machine learning tool for drug design [J]. Journal of Computer-Aided Molecular Design. 1994, 8: 635.
[64] G. Klebe, U. Abraham. Comparative molecular similarity index analysis (CoMSIA to study hydrogen bonding properties and to score combinatorial libraries [J]. Journal of Computer-Aid Molecular Design. 1999, 13: 1-10.
[65] G. Klebe, U. Abraham, T. Mietzner. Molecular similarity indexes in a comparative analysis (CoMSIA of drug molecules to correlate and predict their biological activity [J]. Journal of Medicinal Chemistry. 1994, 37: 4310-4146.
[66] M. B?hm, J. Sturzebecher, G. Klebe. Three-dimensional quantitative structure-activity relationship analyses using comparative molecular field analysis and comparative molecular similarity indices analysis to elucidate selectivity differences of inhibitors binding to trypsin, thrombin, and factor Xa [J]. Journal of Medicinal Chemistry. 1999, 42: 458-477.
[67] P. Benedetti, R. Mannhold, G. Cruciani, G. Ottaviani. GRIND/ALMOND investigations on CysLT1 receptor antagonists of the quinolinyl(bridged)aryl type [J]. Bioorganic & Medicinal Chemistry. 2004, 12: 3607-3617.
[68] G. Ermondi, S. Visentin, G. Caron. GRIND-based 3D-QSAR and CoMFA to investigate topics dominated by hydrophobic interactions: The case of hERG Kt channel blockers [J]. European Journal of Medicinal Chemistry. 2009, 44: 1926-1932.
[69] H. Kubinyi. QSAR and 3D QSAR in drug design [J]. Part 1: Methodology. Drug Discovery Today. 1997, 2: 457-467.
[70] A.J. Hopfinger, S. Wang, J.S. Tokarski, B. Jin, M. Albuquerque, P.J. Madhav, C. Duraiswami. Construction of 3D-QSAR models using the 4D-QSAR analysis formalism [J]. Journal of the Ameican Chemical Society. 1997, 119: 10509-10524.
[71] M.G. Albuquerque, E.J. Barreiro, R.B. de Alencastro, A.J. Hopfinger. Four-dimensional quantitative structure-activity relationship analysis of a series of interphenylene 7-oxabicycloheptane oxazole thromboxane A2 receptor antagonists [J]. Journal of Chemical Information and Computer Sciences. 1998, 38: 925-938.
[72] A. Vedani, H. Briem, M. Dobler, H. Dollinger, D.R. McMasters. Multiple conformation and protonation state representation in 4D-QSAR: The neurokinin-1 receptor system [J]. Journal of Medicinal Chemistry. 2000, 43: 4416-4427.
[73] A. Vedani, M. Dober. 5D QSAR: The key for simulating induced fit [J]. Journal of Medicinal Chemistry. 2002, 45: 2139-2149.
[74] A. Vedani, M. Dobler. Multi-dimentinal QSAR in drug research. Predicting binding affinities, toxicity and pharmacokinetic parameters [J]. Progrss in Drug Research. 2000, 55: 105-135.
[75] A. Vedani, M. Dobler, M.A. Lill. Combining protein modeling and 6D-QSAR. Simulating the binding of structurally diverse ligands to the estrogen receptor [J]. Journal of Medicinal Chemistry. 2005, 48: 3700-3703.
[76] E.M. Krovat, T. Steindl, T. Langer, Recent Advances in Docking and Scoring [J]. Current Computer-Aided Drug Design. 2005, 1, 93-102.
[77] D. E. Koshland. Application of a Theory of Enzyme Specificity to Protein Synthesis [J]. Proceeding of the National Academy Sciences. USA. 1958, 44: 98-104.
[78] L.P. Ehrich, M. Nilges, R.C. Wade. The impact of protein flexibility on protein-protein docking [J]. PROTEINS: Structure, Function, and Bioinformatics. 2005, 58: 126-133.
[79] J. Meiler, D. Baker. ROSETTALIGAND: protein-small molecule with full side-chain flexibility [J]. PROTEINS: Structure, Function, and Bioinformatics. 2006, 65: 538-548.
[80] R. Abagyan, M. Totrov. High-Throughput docking for lead generation [J]. Current Opinion in Chemical Biology. 2002, 5: 375-382.
[81] F. Jing, S.H. Kim. Soft docking: matching of molecular surface cubes [J]. Journal of Molecular Biology. 1991, 219: 79.
[82] I. D. Kuntz. Structure-based Strategies for Drug Design and Discovery [J]. Science. 1992, 257: 1078-1082.
[83] D.S. Goodsell, A.J. Olson. Automated docking of substrates to proteins by simulated annealing [J]. Proteins. 1990, 8: 195-202.
[84] I.D. Kuntz, J.M. Blaney, S.J. Oatley, R. Langridge, T.E. Ferrin A. geometric approach to macromolecule-ligand interactions [J]. Journal of Molecular Biology. 1982, 161: 269-288.
[85] A.J. Olson, D.S. Goodsell. Automated docking and the search for HIV protease inhibitors [J].SAR and QSAR in Environmental Research. 1998, 8: 273-285.
[86] G.M. Morris, D.S. Goodsell, R.S. Halliday, R. Huey, W.E. Hart, R.K. Beiew, A.J. Olson. Automated Docking Using a Lamarekian Genetic Algorithm and Empirical Binding Free Energy Function [J]. Journal of Computational Chemistry. 1998, 19: 1639-1662.
[87] G.M. Morris, R. Huey, W. Lindstrom, M.F. Sanner, R.K. Belew, D.S. Goodsell, A.J. Olson.AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility [J]. Journal of Computational Chemistry. 2009, 30: 2785-2791.
[88] H.J. B?hm. The computer program LUDI: A new method for the de novo design of enzyme inhibitors [J]. Journal of Computer-Aided Molecular Design. 1992, 6: 61-78.
[89] M. Rarey, B. Kramer, T. Lengauer. Docking of hydrophobic ligand with interaction-based matching algorithms [J]. Bioinformatics. 1999, 15: 243-250.
[90] Affinity User Guide [M]. MSI Inc., San Diego. USA . 2002.
[91] B.A. Luty, Z.R. Wassennan, P.F.W. Stouten, C.N. Hodge, M. Zacharias, J.A. McCammon. A Molecular Mechanics/Grid Method for Evaluation of Ligand-Receptor Interactions [J]. Journal of Computational Chemistry. 1995, 16: 454-464.
[92] P.F.W. Stouten, C.Frommel, H. Nakamura, C.Sander. An Effective Solvation Term Based on Atomic Occupancies for Use in Protein Simulations [J]. Molecular Simulattion. 1993, 10: 97-120.
[93] M. Stahl, H.J. B?hm. Development of Filter Functions for Protein-Ligand Docking [J]. Journal of Molecular Graphics and Modelling. 1998,16:121-132.
[94] SYBYL, version 7.3 [M], Tripos Associates, St. Louis, MO. URL www.tripos.com.
[95] R. Judson. Genetic Algorithms and Their Use in Chemistry [J]. In: Lipkowitz K B, Boyd D B, ed. Reviews in Computational Chemistry, Vol 10. New York: VCH Publishers, 1997.
[96] A.N. Jain. Surflex: Fully Automatic Flexible Molecular Docking Using a Molecular Similarity-Based Search Engine [J]. Journal of Medicinal Chemistry. 2003, 46, 499-511.
[97] T.A. Pham, A.J. Jain. Parameter Estimation for Scoring Protein-Ligand Interactions Using Negative Training Data [J]. Journal of Medicinal Chemistry. 2006, 49: 5856-5868.
[98] A.N. Jain, Virtual screening in lead discovery and optimization [J]. Current Opinion in Drug Discovery & Development. 2004, 7: 396-403.
[99] A.N. Jain. Scoring noncovalent protein-ligand interactions: A continuous differentiable function tuned to compute binding affinities [J]. Journal of Computer-Aided Molecular Design. 1996, 10: 427-440.
[100] E. Kellenberger, J. Rodrigo, P. Muller, D. Rognan. Comparative Evaluation of Eight Docking Tools for Docking and Virtual Screening Accuracy [J]. PROTEINS: Structure, Function, and Bioinformatics. 2004, 57: 225-242.
[101] M. Krier, J.X. de Araújo-Júnior, M. Schmitt, J. Duranton, H. Justiano-Basaran, C. Lugnier, J.J. Bourguignon, D. Rognan. Design of Small-Sized Libraries by Combinatorial Assembly of Linkers and Functional Groups to a Given Scaffold: Application to the Structure-Based Optimization of a Phosphodiesterase 4 Inhibitor [J]. Journal of Medicinal Chemistry. 2005, 48:3816-3822.
[102] C. Bissantz, G. Folkers, D. Rognan. Protein-based virtual screening of chemical databases. 1. Evaluation of different docking/scoring combinations [J]. Journal of Medicinal Chemistry. 2000, 43: 4759-4767.
[103] More publications at: http://www.biopharmics.com/publications.html.
[104] A. N. Jain. Effects of protein conformation in docking: improved pose prediction through protein pocket adaptation [J]. J Comput Aided Mol Des. 2009, 23: 355-374.
[105] T.A. Pham, A.N. Jain. Customizing scoring functions for docking [J]. Journal of Computer-Aided Molecular Design. 2008, 22:269-286.
[106] S. Reddy, S.P. Pati, P.P. Kumar, H.N. Pradeep, G.N. Sastry. Virtual Screening in Drug Discovery– A Computational Perspective [J]. Current Protein and Peptide Science. 2007, 8: 329-351
[107] J. Ruppert, W. Welch, A.N. Jain. Automatic identification and representation of protein binding sites for molecular docking [J]. Protein Science. 1997, 6: 524-533.
[108] A.N. Jain. Scoring noncovalent protein-ligand interactions: A continuous differentiable function tuned to compute binding affinities [J]. Journal of Computer-Aided Molecular Design. 1996, 10: 427-440.
[109] W. Welch, J. Ruppert, A.N. Jain. Hammerhead: Fast, fully automated docking of flexible ligands to protein binding sites. Chemical Biology. [J] 1996, 3, 449-462.
[110] R.D. Clark, A. Strizhev, J.M. Leonard, J.F. Blake, J.B. Matthew. Consensus scoring for ligand/protein interactions [J]. Journal of Molecular Graphics and Modelling. 2002, 20: 281.
[111] H.J. B?hm. The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure [J]. Journal of Computer-Aided Molecular Design. 1994, 8: 243-256.
[112] M. Rarey, B. Kramer, T. Lengauer, G. Klebe. A fast flexible docking method using an incremental construction algorithm [J]. Journal of Molecular Biology. 1996, 261, 470-489.
[113] R. Wang, Y. Lu, S. Wang. Comparative Evaluation of 11 Scoring Functions for Molecular Docking [J]. Journal of Medicinal Chemistry. 2003, 46: 2287-2303.
[114] G. Jones, P. Willet, R.C. Glen, A.R. Leach, R. Taylor. Development and validation of a genetic algorithm for flexible docking [J]. Journal of Molecular Biology. 1997, 267: 727.
[115] I. Muegge, Y.C. Martin.A general and fast scoring function for protein-ligand interactions: a simplified potential approach [J]. Journal of Medicinal Chemistry. 1999, 42: 791-804.
[116] I.D. Kuntz, J.M. Blaney, S.J. Oatley, R. Langridge, T.E. Ferrin. A geometric approach to macromolecule-ligand interactions [J]. Journal of Molecular Biology. 1982, 161: 269-288.
[117] M.D. Eldridge, C.W. Murray, T.R. Auton, G.V. Paolini, R.P. Mee. Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes [J]. Journal of Computer-Aided Molecular Design. 1997, 11: 425-445.
[118] S.T. Louis. HQSAR is the product of Tripos Inc [M]. Molecular pharmacology. 1997.
[119] A.A. San, S.J. Cho, H. Cho. HQSAR study of microsomal prostaglandin E-2 synthase (mPGES-1) inhibitors [J]. Bulletin of the Korean Chemical Society. 2006, 27: 1531-1536.
[120] K. Srikanth, P.C. Nair, M.E. Sobhia. Probing the structural and topological requirements for CCR2 antagonism: Holographic QSAR for indolopiperidine derivatives [J]. Bioorganic & Medicinal chemistry letters. 2008, 18:1450-1456.
[121] L.G. Ferreira, A. Leitao, C.A Montanari, A.D Andricopulo. Hologram quantitative structure-activity relationships for a class of inhibitors of HIV-1 protease [J]. Letters in Drug Design & Discovery. 2007, 4: 356-364.
[122] T.L Moda, C.A Montanari, A.D Andricopulo. In silico prediction of human plasma protein binding using hologram QSAR [J]. Letter in Drug Design & Discovery. 2007, 4: 502-509.
[123] N. Masahiro. Toxicity prediction of chemicals base on structure-activity relationships [J]. Toxicology Letters. 1998, 103: 627-629.
[124] P. Zhou, F.F. Tian, Z.L. Li. Three dimensional holographic vector of atomic interaction field (3D-HoVIAF) [J]. Chemometr. Intell. Lab. Syst. DOI: 10.1016/ j.chemolab.2006.10.002.
[125] Z. Li, J. Sun, G. Liang. On three-dimensional holographic vector of atomic interaction field analysis for influenza neuraminidase inhibitors [J]. Chemical Biology & Drug Design. 2009, 73: 236-243.
[126] M. Levitt Protein folding by restrained energy minimization and molecular dynamics [J]. Journal of Molecular Biology. 1983, 170: 723-764.
[127] M. Levitt, M.F. Perutz. Aromatic rings act as hydrogen bond acceptors [J]. Journal of Molecular Biology. 1988, 201: 751-754.
[128] M. Hahn Receptor surface models. 1. definition and construction [J]. Journal of Medicinal Chemistry. 1995, 38: 2080-2090.
[129] R. Wang, Y. Fu, L. Lai. A new atom-additive method for calculating partition coefficients [J]. Journal of Chemical Information and Computer Science. 1997, 37: 615-621.
[130] A. Tadashi, M. Toshiyuki, Y. Ichiro. A New implicit solvent model for brownian dynamics simulation: solvent-accessible surface area dependent effective charge model [J]. Journal of Computer Chemistry. Japan. 2004, 3: 129-136.
[131] D.J. Abraham, G.E. Kellogg, J.M. Holt, G.K. Ackers. Hydropathic analysis of the noncovalentinteractions between molecular subunits of structurally characterized hemoglobins [J]. Journal of Molecular Biology. 1997, 272: 613-632.
[132] T.G. Gunnar, R.D. Umesh. Hydropathic interaction analyses of small organic activators binding to antithrombin [J]. Bioorganic & Medicinal Chemistry. 2004, 12: 633-640.
[133] G.E. Kellogg, D.G. Abraham. Hydrophobicity: is LogPo/w more than the sum of its parts [J]. European Journal of Medicinal. Chemistry. 2000, 35: 651-661.
[134] F.C. Wireko, G.E. Kellogg, D.J. Abraham. Allosteric modifiers of hemoglobin. crystallographically determined binding sites and hydrophobic binding/interaction analysis of novel hemoglobin oxygen effectors [J]. Journal of Medicinal Chemistry. 1991, 34: 758-767.
[135] G.E. Kellogg, G.S. Joshi, D.J. Abraham. New tools for modeling and understanding hydrophobicity and hydrophobic interactions [J]. Medicinal Chemistry Research. 1992, 1: 444-453.
[136] G.E. Kellogg, D.J. Abraham. KEY, LOCK, and LOCKSMITH. Complementary hydrophobicity map predictions of drug structure from a known receptor/receptor structure from known drugs [J]. Journal of Molecular Graphics. 1992, 10: 212-217.
[137] W. Hasel, T.F. Hendrikson, W.C. Still. A rapid approximation to the solvent accessible surface areas of atoms [J]. Tetrahed Comp. Method, 1988, 1: 103-116.
[138] J. Pei, Q. Wang, J. Zhou, L. Lai. Estimating protein-ligand binding free energy: atomic solvation parameters for partition coefficient and solvation free energy calculation [J]. Proteins. 2004, 57: 651-664.
[139] M. Pastor, G. Cruciani, I. McLay, S. Pickett, Sergio Clementi. Grid Independent Descriptors (GRIND) [J]. A Novel Class of Alignment-Independent Three-Dimensional Molecular Descriptors. Journal of Medicinal Chemistry. 2000, 43: 3233-3243.
[140] D. Riganelli, R. Valigi, G. Costantino, M. Baroni, S. Wold. Autocorrelation as a Tool for a Congruent Description of Molecules in 3D QSAR Studies [J]. Pharmaceutical Pharmacological Letters. 1993, 3: 5-8.
[141] F. Fontaine, M. Pastor, F. Sanz. Incorporating Molecular Shape into the Alignment-free GRid-INdependent Descriptors (GRIND) [J]. Journal of Medicinal Chemistry. 2004, 47: 2805-2815.
[142] P.J. Goodford. A computational procedure for determining energetically favourable binding sites on biologically important macromolecules [J]. Journal of Medicinal Chemistry. 1985, 28: 849-857.
[143] GRID v.22 Molecular Discovery Ltd., http://www.moldiscovery.com.
[144] R.D. Clark, E. Abrahamian, C. Abrams, P. Brandt, A.L. Gustavsson, E. Homan, N. Richmond,A. Strizhev, M. Wirstam, and P. Wolohan, Genetic Algorithm with Linear Assignment for Hypermolecular Alignment of Datasets [J]. manuscript in preparation.
[145] E. Abrahamian, P.C. Fox, L. N?rum, I. T. Christensen, H. Th?gersen, R.D. Clark. [J]. Journal of Chemical Information and Computer Science. 2003, 43: 458-468.
[146] V.J. Gillet, P. Willett, P.J. Fleming, D.V.S. Green. Designing focused libraries using MoSELECT [J]. Journal of Molecular Graphics and Modelling. 2002, 20: 491-498.
[147] S.J. Cottrell, V.J. Gillet, R. Taylor, D.J. Wilton. Generation of Multiple Pharmacophore Hypotheses using multiobjective optimisation techniques [J]. Journal of Computed-Aided Molecular Design. 2004: 18: 665-682.
[148] N.J. Richmond, P. Willett, R.D. Clark. Alignment of three-dimensional molecules using an image recognition algorithm [J]. Journal of Molecular Graphics and Modelling. 2004, 23: 199-209.
[149] G. Cruciani, M. Pastor, S. Clementi. Handling information from 3D grid maps for QSAR studies. Molecular Modeling and Prediction of Bioactivity. K. Gundertofte and F.E. J?rgensen (eds. Kluwer Academic/ Plenum Publishers [J]. New York. 2000, 73-82.
[150] G. Cruciani, P. Crivori, P.A. Carrupt, B. Testa. Molecular Fields in Quantitative Structure-Permeation Relationships: the VolSurf Approach [J]. Journal of Molecular Structure: THEOCHEM. 2000, 503: 17-30.
[151] P. Crivori, G. Cruciani, P.A. Carrupt, B. Testa. Predicting Blood-Brain Barrier Permeation from Three-Dimensional Molecular Structure [J]. Journal of Medicinal Chemistry. 2000, 43: 2204-2216.
[152] G. Cruciani, M. Meniconi, E. Carosati, I. Zamora, R. Mannhold. VOLSURF: a tool for drug ADME-properties prediction [J]. In Drug Bioavailability, Methods and Principles in Medicinal Chemistry. Edited by Van de Waterbeemd H, Lennerna¨s H, Artursson P. Weinheim: Wiley-VCH. 2003, 18: 406-419.
[153] T. I. Oprea, I. Zamora, A.L. Ungell. Pharmacokinetically Based Mapping Device for Chemical Space Navigation [J]. Journal of Combinatorial Chemisry. 2002; 4: 258-266.
[154] F. Lombardo, R.S. Obach, M.Y. Shalaeva, F. Gao. Prediction of Volume of Distribution Values in Humans for Neutral and Basic Drugs Using Physicochemical Measurements and Plasma Protein Binding Data [J]. Journal of Medicinal Chemistry. 2002, 45: 2867-2876.
[155] R. Pearlstein, R. Vaz; D. Rampe. Understanding the Structure?Activity Relationship of the Human Ether-a-go-go-Related Gene Cardiac K+ Channel. A Model for Bad Behavior [J]. Journal of Medicinal Chemistry. 2003, 46: 2017-2022.
[156] P. Crivori, I. Zamora, B. Speed, C. Orrenius, I. Poggesi. Model based on GRID-deriveddescriptors for estimating CYP3A4 enzyme stability of potential drug candidates [J]. Joural of Computer-Aided Molecular Design. 2004, 18: 155-166.
[157] D.B. Hibbert. Genetic algorithms in chemistry [J]. Chemometrics and Intelligent Laboratory Systems. 1993, 19: 277-293.
[158] J.H. Holland. Adaptation in natural and artificial systems [J]. Ann Arbor: University of Michigan Press. 1975: 156-159.
[159] D. Goldberg. Genetic algorithms in search, optimization, and machine learning [J]. New York: Addison-Wesley Publishing Company, Inc. 1989: 49-53.
[160] S.Wold, A.Ruhe, H. Wold. The collinearity problem in linear regression. The partial least squares approach to generalized inverses [J]. SIAM Journal of Scientific and Stattistical Computing. 1984, 5: 735-743.
[161] P. Nomikos, J.F. MacGregor. Multi-way partial least squares in monitoring batch processes [J]. Chemometrics and Intelligent Laboratory Systems. 1995, 30:97-108.
[162] R.J. Ergon. PLS score-loading correspondence and a biorthogonal factorization [J]. Chemometrics. 2002, 16: 368-373.
[163] S.D. Jong. SIMPLS: An alternative approach to partial least squares regression [J]. Chemometrics and Intelligent Laboratory Systems. 1993, 18: 251-263.
[164] J.T. Leonard, K. Roy. On selection of training and test sets for the development of predictive QSAR models [J]. QSAR & Combinatorial Science. 2006, 25: 235-251.
[165] G. A.Tropsha. Beware of q2 [J]. Journal of Molecular Graphics Modelling. 2002, 20: 269-276.
[166] P.Gramatica, P.Pilutti, E.Papa. Validated QSAR prediction of OH tropospheric degradation of VOCs: Splitting into training-test sets and consensus modeling [J]. Journal of Chemical Information Computer Science. 2004, 44:1794-1802.
[167] C. S?antai-Kis, I. K?vesdi, G. Kéri, L. ?rfi, Validation subset selections for extrapolation oriented QSPAR models [J]. Molecular Diversity. 2003, 7: 37-43.
[168] SYBYL'Leapforg Manual' version 7.3 [M]. Tripos, Inc., St. Louis, MO.
[169] D. Kuntz, J. M. Blaney, S. J. Oatley, R. Langridge, T. Ferrin. A Geometric Approach to Macromolecular-Ligand Interactions [J]. Journal of Molecular Biology. 1982, 161: 269-288.
[170] J. B. Moon, W. J. Howe. Computer Design of Bioactive Molecules: A Method for Receptor-Based de novo Ligand Design [J]. Proteins. 1991, 11: 314-328.
[171] V. Gillet, A.P. Johnson, P. Mata, S. Sike, P. Williams. SPROUT: A Program for Structure Generation [J]. Tet. Computer Methods. 1992, 1-34.
[172] H. J. Boehm. The Computer Program LUDI: A New Method for de novo Design of Enzyme Inhibitors [J]. Journal of Computer-Aided Molcular Design. 1992, 6: 61-78.
[173] W. R. Payne, R.C. Glen. Molecular Recognition Using a Binary Genetic Search Algorithm [J]. Journal of Molecular Graphics. 1993, 11: 74.
[174] Y.C. Martin. 3D Database Searching in Drug Design [J]. Journal of Medicinal Chemistry. 1992, 35: 2145-2154.
[175] D.J. Danziger, P.M. Dean. Automated Site-Directed Drug Design: The Prediction and Observation of Ligand Point Positions at Hydrogen-Bonding Positions on Proteins Surfaces [J]. Proceedings the Roral of Society. 1989, B236: 115-124.
[176] Miranker, M. Karplus. Functionality maps of binding sites: a multiple copy simultaneous search method [J]. Proteins, 1991, 11: 29-34.
[177] Y. Nishibata, A. Atai. Automatic Creation of Drug Candidate Structures Based on Receptor Structure. Starting Point for Artificial Lead Generation [J].Tetrahedron. 1991, 47: 8985.
[178] C.M.W. Ho, G.R. Marshall. FOUNDATION: A program to retrieve all possible structures containing a suer-defined minimum number of matching query elements from three-dimensional databases [J]. Journal of Computer-Aided Molcular Design. 1993, 7: 3-23.
[179] D. S. Goodsell, A. J. Olson. Automated Docking of Substrates to Proteins by Simulated Annealing [J]. Proteins 1990. 8: 195-202.
[180] M. C. Lawrence, P.C. Davis. CLIX: A Search-Algorithm for Finding Novel Ligands Capable of Binding Proteins of Known Three-Dimensional Structure [J]. Proteins. 1992, 12: 31-41.
[181] C.L.M.J. Verlinde, G. Rudenko, W. G. Hol. In Search of New Lead Compounds for Trypanosmiasis: A Protein Structure-Based Linked-Fragment Approach [J]. Journal of Computer-Aided Molcular Design. 1992, 6: 131-147.
[182] J. Singh, J. Saldanha, J.M. Thornton, A Novel Method for the Modeling of Peptide Ligands to their Receptors [J]. Protein Engineering. 1991, 4: 251-261.
[183] S.H. Rotstein, M.A. Murcko. GenStar 1.0: A Method for de novo Drug Design [J]. Journal of Computer-Aided Molcular Design. 1993, 7: 23-43.
[184] R.A. Lewis, D.C. Roe, C. Huang, T.C. Ferrin, R. Langridge, I.D. Kuntz. Automated. Site-Directed Drug Design Using Molecular Lattices [J]. Journal of Molecular Graphics and modeling. 1992, 10: 66-67.
[185] D.J. Bacon, J.Moult. Docking by Least-Squares Fitting of Molecular Surface Patterns [J]. Journal of Molecular Biology. 1992, 225: 849-858.
[186] S. Dixon, J. Blaney, D. Weininger. Presented at the Third York Meeting—Characterizing and Satisfying the Steric and Chemical Restraints of Binding Sites [J]. 1993, March: 29-30.
[187] R. D. Cramer III, D. E. Patterson, J. D. Bunce. Comparative Molecular Field Analysis (CoMFA). Effect of Shape on Binding of Steroids to Carrier Proteins [J]. Journal of theAmerican Chemistry Society. 1988, 110: 5959-5967.
[188] M. Clark, R. D. Cramer III, N. Van Opdenbosch. Validation of the General Purpose TRIPOS 5.2 Force Field [J]. Journal of Computational Chemistry. 1989, 10: 982-1012.
[189] R.S. Bohacek, C. McMartin. Definition and Display of Steric, Hydrophobic, and Hydrogen-Bonding Properties of Ligand Binding Sites in Proteins Using Lee and Richards Accessible Surfaces: Validation of a High-Resolution Tool for Drug Design [J]. Journal of Medicinal Chemistry. 1992, 35: 1671-1684.
[190] P. J. Goodford. A Computational Procedure for Determining Energetically Favorable Binding Sites on Biologically Important Macromolecules [J]. Journal of Medicinal Chemistry. 1985, 28: 849-857.
[191] N. Pattabiraman, M. Levitt, T. E. Ferrin, R. Langridge. Computer Graphics in Real-Time Docking with Energy Calculation and Minimization [J]. Journal of Computational Chemistry. 1985, 6: 432-436.
[192] P.C. Nair, E.M. Sobhia. CoMFA based de novo design of pyridazine analogs as PTP1B inhibitors [J]. Journal of Molecular Graphics and Modeling. 2007, 26: 117-123.
[193] A. Kumar, M.I. Siddiqi. CoMFA based de novo design of Pyrrolidine Carboxamides as Inhibitors of Enoyl Acyl Carrier Protein Reductase from Mycobacterium tuberculosis [J]. Journal of Molecular Modeling. 2008, 14: 923-935.
[194] S. Forrest. Genetic Algorithm: Principles of Natural Selection Applied to Computation [J]. Science, 1993, 261: 872-878.
[195] R. Dawkins. The Selfish Gene [J]. Oxford University Press, Oxford, UK, 1976.
[196] Y. C. Martin. 3D database searching in drug design. [J]. Journal of Medicianl Chemistry 1992, 35: 2145.-2154.
[197] S.M. Wang, D.W. Zaharevitz, R. Sharma, V.E. Marquez, N.E. Lewin, L. Du, P.M. Blumberg, G.W.A. Milne. The Discovery of Novel, Structurally Diverse Protein Kinase C Agonists through Computer 3D-Database Pharmacophore Search. Molecular Modeling Studies [J]. Journal of Medicianl Chemistry. 1994, 37: 4479-4489.
[198] Y. Kurogi, O. F. Guner. Pharmacophore modeling and three-dimensional database searching for drug design using catalyst [J]. Current Medicinal Chemistry. 2001, 8: 1035-1055.
[199] S. Chun, J.Y. Lee, S.G. Ro, K.W. Jeong, Y. Kim, C.J. Yoon. 3D Quantitative and Qualitative Structure-Activity Relationships of theα-Opioid Receptor [J]. Antagonists Bulletin of the Korean Chemical Society. 2008, 29: 656-662.
[200] K.C. Tsai, L.W. Teng, Y.M. Shao, Y.C. Chen, Y.C. Lee, M. Li, N.W. Hsiao. The first pharmacophore model for potent NF-kB inhibitors [J]. Bioorganic & Medicinal ChemistryLetters. 2009, 19: 5665-5669.
[201] Sybyl 7.0 manual [M]. Tripos Inc., 1699 South Hanley Rd, St. Louis, MO, U.S.A. 2004.
[202] Y.C. Martin. C.Hansch, T.E. Fujita, D.C. Washington: In classical and 3D QSAR in agrochemistry [J]. American Chemical Society. 1995: 318-329.
[203] G. Jones, P. Willett, R.C. Glen. A genetic algorithm for flexible molecular overlay and pharmacophore elucidation [J]. Journal of Computer-Aided Molecular Design. 1995, 9: 532-549.
[204] T. Langer, G. Wolber. Pharmacophore definition and 3D searches [J]. Drug Discovery Today: Technology. 2004, 1: 203-207.
[205] G.M. Rishton. Reactive compounds and in vitro false positive in HTS [J]. Drug Discovery Today. 1997, 2: 382-384.
[206] C.A. Lipinski, F. Lombardo, B.W. Dominy. et al. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings [J]. Advance Drug Delivery Reviews. 2001, 46: 3-26.
[207] N.R. Taylor, A. Cleasby, O. Singh, T. Skarzynski, A.J. Wonacott, P.W. Smith, S.L. Sollis, P.D. Howes, P.C. Cherry, R. Bethell, P. Colman, J. Varghese. Dihydropyrancarboxamides related to zanamivir: a new series of inhibitors of influenza virus sialidases. 2. Crystallographic and molecular modeling study of complexes of 4-amino-4H-pyran-6-carboxamides and sialidase from influenza virus types A and B [J]. Journal of Medicinal Chemistry. 1998, 41: 798-807.
[208] B.J. Smith, J.L. McKimm-Breshkin, M. McDonald, R.T. Fernley, J.N. Varghese, P.M. Colman. Structural studies of the resistance of influenza virus neuramindase to inhibitors [J]. Journal of Medicinal Chemistry. 2002, 45: 2207-2212.
[209] J.N. Varghese, V.C. Epa, P.M. Colman. Three-dimensional structure of the complex of 4-guanidino-Neu5Ac2en and influenza virus neuraminidase [J]. Protein Science. 1995, 4: 1081-1087.
[210] G.T. Wang, S. Wang, R. Gentles, T. Sowin, C.J. Maring, D.J. Kempf, W.M. Kati, V. Stoll, K.D. Stewart, G. Laver. Design, synthesis, and structural analysis of inhibitors of influenza neuraminidase containing a 2,3-disubstituted tetrahydrofuran-5-carboxylic acid core [J]. Bioorganic & Medicinal Chemistry Letters. 2005, 15: 125-128.
[211] R.J. Russell, L.F. Haire, D.J. Stevens, P.J. Collins, Y.P. Lin, G.M. Blackburn, A.J. Hay, S.J. Gamblin, J.J. Skehel. The structure of H5N1 avian influenza neuraminidase suggests new opportunities for drug design [J]. Nature. 2006, 443: 45-49.
[212] J.N. Varghese, P.W. Smith, S.L. Sollis, T.J. Blick. A. Sahasrabudhe. J.L. McKimm-Breschkin. P.M. Colman. Drug design against a shifting target: a structural basis for resistance toinhibitors in a variant of influenza virus neuraminidase [J]. Structure. 1998, 6: 735-746.
[213] X. Xu, X. Zhu, R.A. Dwek, J. Stevens, I.A. Wilson. Structural characterization of the 1918 influenza virus H1N1 neuraminidase [J]. the Journal of Virology. 2008, 82: 10493-10501.
[214] G.T. Wang, Y.W. Chen, S. Wang, R. Gentles, T. Sowin, W. Kati, S. Muchmore, V. Giranda, K.t Stewart, H. Sham, D. Kempf, W. Graeme Laver. Design, Synthesis, and Structural Analysis of Influenza Neuraminidase Inhibitors Containing Pyrrolidine Cores [J]. Journal of Medicinal Chemistry. 2001, 44:1192-1201.
[215] J. Zhang, Q. Wang, H. Fang, W.F. Xu, A.L. Liu, G.H. Du. Design, synthesis, inhibitory activity, and SAR studies of pyrrolidine derivatives as neuraminidase inhibitors [J]. Bioorganic & Medicinal Chemistry. 2007, 15: 2749-2758.
[216] C.J. Maring, V.S. Stoll, C. Zhao, M.H. Sun, A.C. Krueger, K.D. Stewart, D.L. Madigan, W.M. Kati, Y.B. Xu, R.J. Carrick, D.A. Montgomery, A. Kempf-Grote, K.C. Marsh, A. Molla, K.R. Steffy, H.L. Sham, W. Graeme Laver, Y.G. Gu, D.J. Kempf, W.E. Kohlbrenner, Structure-Based Characterization and Optimization of Novel Hydrophobic Binding Interactions in a Series of Pyrrolidine Influenza Neuraminidase Inhibitors [J]. Journal of Medicinal Chemistry. 2005, 48: 3980-3990.
[217] A.C. Krueger, Y. Xu, W.M. Kati, D.J. Kempf, C.J. Maring, K.F. McDaniel, A. Molla, D. Montgomery, W.E. Kohlbrenner. Synthesis of potent pyrrolidine influenza neuraminidase inhibitors [J]. Bioorganic & Medicinal Chemistry Letters. 2008, 18:1692-1695.
[218] A.M.A. Hammad, F.U. Afifi, M.O. Taha, Combining docking, scoring and molecular field analyses to probe influenza neuraminidase–ligand interactions [J]. Journal of Molecular Graphics and Modelling. 2007, 26: 443-456.
[219] I.D. Wall, A.R. Leach, D.W. Salt, M.G. Ford. J.W. Essex. Binding Constants of Neuraminidase Inhibitors: An Investigation of the Linear Interaction Energy Method [J]. Journal of Medicinal Chemistry. 1999, 42: 5142-5152.
[220] Y. Cheng, W. Prusoff. Relationship between the inhibition constant (Ki and the concentration of inhibitor which causes 50 percent inhibition (I50 of an enzymatic reaction [J]. Biochemical Pharmacology. 1973, 22: 3099-3108.
[221] M.Y. Zheng, K.Q. Yu, H. Liu, X.M. Luo, K.X. Chen , W.L. Zhu and H.L. Jiang, QSAR analyses on avian influenza virus neuraminidase inhibitors using CoMFA, CoMSIA, and HQSAR [J]. Journal of Computer-Aided Molecular Design. 2006, 20: 549-566.
[222] X.Yi, Z.R. Guo, F.M. Chu. Study on Molecular Mechanism and 3D-QSAR of Influenza Neuraminidase Inhibitors [J]. Bioorganic & Medicinal Chemistry. 2003, 11: 1465-1474.
[223] S. Renner, S. Derksen, S. Radestock, F. M?rchen. Maximum Common Binding Modes(MCBM: Consensus Docking Scoring Using Multiple Ligand Information and Interaction Fingerprints [J]. Journal of Chemical Information and Modeling. 2008, 48: 324.
[224] R.D. Clark, P.C. Fox. Statistical variation in progressive scrambling [J]. Journal of Computer-Aided Molecular Design. 2004, 18: 563–576.
[225] C.W. Sun, X.D. Zhang, H. Huang, P. Zhou. Synthesis and evaluation of a new series of substituted acyl(thio)urea and thiadiazolo [2,3-a] pyrimidine derivatives as potent inhibitors of influenza virus neuraminidase [J]. Bioorganic & Medicinal Chemistry. 2006, 14: 8574-8581.
[226] J. Sun, S. Cai, J. Li, N. Yan, Y. Wang, H. Mei. Docking and 3D QSAR study of thiourea analogues as potent inhibitors of influenza virus neuraminidase [J]. Journal of Molecular Modeling. DOI: 10.1007/s00894-010-0685-9.
[227] M. Clark, R.D. Crame. The probability of chance correlation using partial least squares (PLS) [J]. Quantitative Structure-Activity Relationships. 1993, 2: 137-145.
[228] S.W. Yao, V.H.C. Lopes, F. Fernández, X. García-Mera, M. Morales, J.E. Rodríguez-Borges, M.N.D.S. Cordeiro. Synthesis and QSAR study of the anticancer activity of some novel indane carbocyclic nucleosides [J]. Bioorganic & Medicinal Chemistry. 2003, 11: 4999-5006.
[229] A.L. Liu, H.D. Wang, S.M.Y. Lee, Y.T. Wang, G.H. Du. Structure-activity relationship of flavonoids as influenza virus neuraminidase inhibitors and their in vitro anti-viral activities [J]. Bioorganic & Medicinal Chemistry. 2008, 16: 7141-7147.
[230] K. Miki, T.Nagai, K. Suzuki, R. Tsujimura, K. Koyama, K. Kinoshita, K. Furuhata, H. Yamada, K. Takahashi. Anti-influenza virus activity of biflavonoids [J]. Bioorganic & Medicinal Chemistry Letters. 2007, 17: 772-775.
[231] Y.B. Ryu, M.J. Curtis-Long, J.H. Kim, S.H. Jeong, M.S. Yang, K.W. Lee, W.S. Lee, K.H. Park. Pterocarpans and flavanones from Sophora flavescens displaying potent neuraminidase inhibition [J]. Bioorganic & Medicinal Chemistry Letters. 2008, 18: 6046-6049.
[232] Y.B. Ryu, M.J. Curtis-Long, J.W. Lee, J.H. Kim, J.Y. Kim, K.Y. Kang, W.S. Lee, K.H. Park. Characteristic of neuraminidase inhibitory xanthones from Cudrania tricuspidata [J]. Bioorganic & Medicinal Chemistry. 2009, 17: 2744-2750.
[233] W. Lew, H. Wu, X. Chen, B.J. Graves, P.A. Escarpe, H.L. MacArthur, D.B. Mendel, C.U. Kim. Carbocyclic Influenza Neuraminidase Inhibitors Possessing a C3-Cyclic Amine Side Chain: Synthesis and Inhibitory Activity [J]. Bioorganic & Medicinal Chemistry Letters. 2000, 10: 1257-1260.
[234] W. Lew, H. Wu, D.B. Mendel, P.A. Escarpe, X. Chen, W.G. Laver, B.J. Graves, C.U. Kim. A new series of c3-aza carbocyclic influenza neuraminidase inhibitors: synthesis and inhibitoryactivityl [J]. Bioorganic & Medicinal Chemistry Letters.1998, 8: 3321-3324.
[235] C.U. Kim, W. Lew, M.A. Williams, H. Liu, L. Zhang, S. Swaminathan, N. Bischofberger, M.S. Chen, D.B. Mendel, C.Y. Tai, W.G. Laver, R.C. Stevens. Influenza Neuraminidase Inhibitors Possessing a Novel Hydrophobic Interaction in the Enzyme Active Site: Design, Synthesis, and Structural Analysis of Carbocyclic Sialic Acid Analogues with Potent Anti-Influenza Activity [J]. Journal of the American Chemistry Society. 1997, 119: 681-690.
[236] C.U. Kim, W. Lew, M.A. Williams, H. Wu, L. Zhang, X. Chen, P.A. Escarpe, D.B. Mendel, W. G. Laver, R.C. Stevens. Structure-Activity Relationship Studies of Novel Carbocyclic Influenza Neuraminidase Inhibitors [J]. Journal of Medicinal Chemistry. 1998, 41: 2451-2460.
[237] M.A. Williams, W. Lew, D.B. Mendel, C.Y. Tai, P.A. Escarpe, W.G. Laver, R.C. Stevens, C.U. Kim. Structure-activity relationships of carbocyclic influenza neuraminidase inhibitors [J]. Bioorganic & Medicinal Chemistry Letters. 1997, 7: 1837-1842.
[238] P. Chand, P.L. Kotian, P.E. Morris, S. Bantia, D.A. Walsh, Y.S. Babua. Synthesis and inhibitory activity of benzoic acid and pyridine derivatives on influenza neuraminidase [J]. Bioorganic & Medicinal Chemistry. 2005, 13: 2665-2678.
[239] J. Zhang, Q. Wang, H. Fang, W. Xu, A. Liu, G. Du. Design, synthesis, inhibitory activity, and SAR studies of hydrophobic p-aminosalicylic acid derivatives as neuraminidase inhibitors [J]. Bioorganic & Medicinal Chemistry. 2008, 16: 3839-3847.
[240] P.D. Howes, A. Cleasby, D.N. Evans, H. Feilden, P.W. Smith, S.L. Sollis, N. Taylor, A.J. Wonacott. 4-acetylamino-3-(imidazol-1-yl-benzoic acids as novel inhibitors of influenza sialidase [J]. European journal of medicinal chemistry. 1999, 34: 225-234.
[241] V.R. Atigadda, W.J. Brouillette, F. Duarte, Y.S. Babu, S. Bantia, P. Chand, N. Chu, J.A. Montgomery, D.A. Walsh, E. Sudbeck, J. Finley, G.M. Air, M. Luo, G.W. Laver. Hydrophobic Benzoic Acids as Inhibitors of Influenza Neuraminidase [J]. Bioorganic & Medicinal Chemistry. 1999, 7: 2487-2497.
[242] V.R. Atigadda, W.J. Brouillette, Franco Duarte, Shoukath M. Ali, Yarlagadda S. Babu, Shanta Bantia, Pooran Chand, Naiming Chu, John A. Montgomery, David A. Walsh, Elise A. Sudbeck, James Finley, Ming Luo, Gillian M. Air, Graeme W. Laver. Potent Inhibition of Influenza Sialidase by a Benzoic Acid Containing a 2-Pyrrolidinone Substituent [J]. Journal of Medicinal Chemistry. 1999, 42, 2332-2343.
[243] P. Chand, Y.S. Babu, S. Bantia, N. Chu, L. B. Cole, P.L. Kotian, W. Graeme Laver, J.A. Montgomery, V.P. Pathak, S.L. Petty, D.P. Shrout, D.A. Walsh, G.M. Walsh. Design and Synthesis of Benzoic Acid Derivatives as Influenza Neuraminidase Inhibitors UsingStructure-Based Drug Design1 [J]. Journal of Medicinal Chemistry. 1997, 40: 4030-4052.
[244] P. Chand, Y.S. Babu, S. Bantia, S. Rowland, A. Dehghani, P.L. Kotian, T.L. Hutchison, S. Ali, W. Brouillette, Y. El-Kattan, T. H. Lin. Syntheses and Neuraminidase Inhibitory Activity of Multisubstituted Cyclopentane Amide Derivatives [J]. Journal of Medicinal Chemistry. 2004, 47: 1919-1929.
[245] J.Y. Sun, S.X. Cai, N. Yan, H. Mei. Docking and QSAR studies of influenza neuraminidase inhibitors using three-dimensional holographic vector of atomic interaction field analysis [J]. European Journal of Medicinal Chemistry. 2010, 45: 1008-1014.
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