药物ADMET理论预测方法开发和靶向雌激素受体的药物设计研究
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摘要
本文主要针对目前计算机辅助药物设计中的几个重要问题进行了研究,包括小分子药代动力学性质和毒性(ADMET)理论预测方法的发展和针对雌激素受体进行的相关药物设计研究。论文首先介绍了工作的研究背景和基本概念。在第1章主要介绍了小分子药代动力学理论预测的基本原理和一般方法,包括分子描述、支持向量机、遗传算法等。此外,还介绍了本文涉及到的一些分子模拟和药物设计方法的基本概念和科学原理,包括分子动力学模拟,虚拟筛选等。最后我们还讨论了我国药物设计研究的现状和展望。
小分子的ADMET'性质是药物研发过程中最关键的问题之一。本论文利用化学信息学的基本方法和技术,发展了新的小分子ADMET预测方法：论文第2章介绍了针对ADMET预测建模过程中的变量选择问题,发展了一种基于遗传算法的变量选择方法,在该方法中,我们在适应度评价函数中引入了交叉验证相关系数,并采用了“精英仓库”策略,避免了传统遗传算法中可能出现了较优解变异以及搜寻空间缩小的问题,利用该方法,我们建立小分子化合物血脑分布系数的预测模型；大多数ADMET预测方法都采用了分子描述符作为小分子的描述方法,然而小分子描述符本身存在着一些问题,论文第3章介绍了一种基于子结构模式识别的分类预测方法,我们采用了基于子结构字典的分子指纹描述小分子,从而避免了描述符的使用,利用一种类似模式识别的思想,通过对特征子结构的识别进行建模。同时,我们引入了信息增益方法,分析了每一个子结构的重要性从而可以从药物化学家的角度帮助解释我们所得到的机器学习模型。我们利用该方法建立了小分子肠吸收和血脑屏障通透性的模型,此外我们的方法还应用到毒性以及代谢相关的理论预测研究中。
雌激素受体属于核受体超家族,对维持人体正常生理活动具有非常重要的作用,也是包括乳腺癌等在内的一些重要疾病的靶标之一。目前临床上使用的选择性雌激素受体调节剂,如他莫昔芬,它们可以选择性地作用于不同的组织,虽然机理尚不明确,但在一定程度上减轻了药物的副作用。随着1996年发现了雌激素受体的第二个亚型,人们的注意力开始转移到亚型选择性的小分子调节剂上。近年来该领域的研究主要集中在研究两种亚型的生理功能的区别和各自对应的专一性调控通路。此外,寻找具有亚型选择性的小分子调节剂也是非常热门的研究领域,一方面为功能研究提供小分子探针工具,另一方面可以发现功能更专一,副作用更小的新药。论文的第二部分主要介绍了利用多种分子模拟和计算机辅助药物设计方法,发现选择性雌激素受体的小分子配体。论文第4章主要介绍了利用分子动力学模拟重现了选择性雌激素配体从受体中解离的动态过程,在此过程中,我们发现了雌激素的选择性与这一过程也有着一定的联系。我们据此提出了两个可能提高配体对于ERβ选择性的改造意见。论文第5章中,我们利用虚拟筛选和分子动力学模拟的方法发现了18个高效的ERβ选择性配体,对这些活性化合物的构效关系分析也验证了我们前面提出的改造意见的合理性。本研究将基础理论研究和实验结合起来,理论模型为实验提供了明确的指导方向,而实验结果又进一步验证了理论模型。
This thesis concentrated on some important problems existing in computer aided drug design, including two parts:(1) methodology development of in silico ADMET prediction; and (2) drug design targeting estrogen receptors. In the first chapter, a brief introduction about the research background and basic concepts was given, including genetic algorithms, support vector machine, molecular fingerprint, etc. Emphases were put on those concepts and principles tightly related to this thesis, such as molecular dynamics simulation, molecular docking and scoring. Finally, we discussed the current situation of drug design research in China.
ADMET is one of the most important issues in drug discovery pipeline. With technical improvement, people realized that it was not difficult to find potent and specific leads in the early stage of drug discovery. However, experimental evaluation of ADMET properties can still not meet the demands of lead discovery and optimization due to the time-and cost-effectiveness. Therefore, in silico ADMET prediction has become a practicable alternative choice so far, which could break through the "bottleneck" in the high throughput drug discovery process. The first part of this thesis is developing new methodology for in silico ADMET prediction. The second chapter described a genetic algorithm based variable selection method. In this method, an improved fitness function involving with the cross validation coefficients was used, and we also designed an elite warehouse to avoid the pitfall of conventional genetic algorithms. Then the logBB models were built as the case study. Molecular descriptors were widely used in conventional ADMET prediction methodologies. However, there are several shortcomings of using molecular descriptors. The third chapter described a chemical classification method based on substructure recognition. Therefore the molecular descriptors were avoided during the model building. In addition, information gain analysis was involved to evaluate each substructure of the molecules, which could help to interpret the machine learning models from medicinal chemistry perspective. We also presented their applications in the ADMET prediction.
Estrogen receptors (ERs) belong to nuclear receptor superfamily, which play crucial roles in human body, including the growth and differentiation of reproductive system, central nervous system and skeletal system. They are also important drug target for some diseases, including breast cancer. At present, most clinic use drugs are selective ER modulator. Although the underlying mechanism remains unclear, they have indeed alleviated the side effect of drugs. The second subtype, ERβ, which was discovered in 1996, shedding light on discovering subtype selective ligands for achieving tissue selective drugs. Therefore, many recent works focused on the differences in biological functions and pathways between the two subtypes. Besides, finding novel selective ligands could not only benefit the drug development, but also provide the chemical sensor for biological researches. The second section of this thesis is focusing on discovering selective ERβligands with various molecular modeling and CADD methods. In chapter 4, we performed steered molecular dynamics simulation to investigate the dynamic process of ligand unbinding from two different subtypes of ERs. In this work, we firstly find out that the process of ligand unbinding also contribute to the ligand selectivity. Accordingly, we proposed two suggestions for improving ERP selectivity. In chapter 5, we discovered 18 potent ligands of ERβwith virtual screening based on a structure optimized through molecular dynamics simulation. Among them, dual profile was observed in two ligands, as agonists for ERβand antagonists for ERa, which might be served as lead compounds for selective ER modulators. The results also suggest that structures optimized through MD are applicable to lead discovery. Besides, the structure activity relationship results confirmed the suggestions proposed in chapter 4. In these work, we presented an integrated work which involved theoretic calculations and experiments. The results have been verified for each other.
小分子的ADMET'性质是药物研发过程中最关键的问题之一。本论文利用化学信息学的基本方法和技术,发展了新的小分子ADMET预测方法：论文第2章介绍了针对ADMET预测建模过程中的变量选择问题,发展了一种基于遗传算法的变量选择方法,在该方法中,我们在适应度评价函数中引入了交叉验证相关系数,并采用了“精英仓库”策略,避免了传统遗传算法中可能出现了较优解变异以及搜寻空间缩小的问题,利用该方法,我们建立小分子化合物血脑分布系数的预测模型；大多数ADMET预测方法都采用了分子描述符作为小分子的描述方法,然而小分子描述符本身存在着一些问题,论文第3章介绍了一种基于子结构模式识别的分类预测方法,我们采用了基于子结构字典的分子指纹描述小分子,从而避免了描述符的使用,利用一种类似模式识别的思想,通过对特征子结构的识别进行建模。同时,我们引入了信息增益方法,分析了每一个子结构的重要性从而可以从药物化学家的角度帮助解释我们所得到的机器学习模型。我们利用该方法建立了小分子肠吸收和血脑屏障通透性的模型,此外我们的方法还应用到毒性以及代谢相关的理论预测研究中。
雌激素受体属于核受体超家族,对维持人体正常生理活动具有非常重要的作用,也是包括乳腺癌等在内的一些重要疾病的靶标之一。目前临床上使用的选择性雌激素受体调节剂,如他莫昔芬,它们可以选择性地作用于不同的组织,虽然机理尚不明确,但在一定程度上减轻了药物的副作用。随着1996年发现了雌激素受体的第二个亚型,人们的注意力开始转移到亚型选择性的小分子调节剂上。近年来该领域的研究主要集中在研究两种亚型的生理功能的区别和各自对应的专一性调控通路。此外,寻找具有亚型选择性的小分子调节剂也是非常热门的研究领域,一方面为功能研究提供小分子探针工具,另一方面可以发现功能更专一,副作用更小的新药。论文的第二部分主要介绍了利用多种分子模拟和计算机辅助药物设计方法,发现选择性雌激素受体的小分子配体。论文第4章主要介绍了利用分子动力学模拟重现了选择性雌激素配体从受体中解离的动态过程,在此过程中,我们发现了雌激素的选择性与这一过程也有着一定的联系。我们据此提出了两个可能提高配体对于ERβ选择性的改造意见。论文第5章中,我们利用虚拟筛选和分子动力学模拟的方法发现了18个高效的ERβ选择性配体,对这些活性化合物的构效关系分析也验证了我们前面提出的改造意见的合理性。本研究将基础理论研究和实验结合起来,理论模型为实验提供了明确的指导方向,而实验结果又进一步验证了理论模型。
This thesis concentrated on some important problems existing in computer aided drug design, including two parts:(1) methodology development of in silico ADMET prediction; and (2) drug design targeting estrogen receptors. In the first chapter, a brief introduction about the research background and basic concepts was given, including genetic algorithms, support vector machine, molecular fingerprint, etc. Emphases were put on those concepts and principles tightly related to this thesis, such as molecular dynamics simulation, molecular docking and scoring. Finally, we discussed the current situation of drug design research in China.
ADMET is one of the most important issues in drug discovery pipeline. With technical improvement, people realized that it was not difficult to find potent and specific leads in the early stage of drug discovery. However, experimental evaluation of ADMET properties can still not meet the demands of lead discovery and optimization due to the time-and cost-effectiveness. Therefore, in silico ADMET prediction has become a practicable alternative choice so far, which could break through the "bottleneck" in the high throughput drug discovery process. The first part of this thesis is developing new methodology for in silico ADMET prediction. The second chapter described a genetic algorithm based variable selection method. In this method, an improved fitness function involving with the cross validation coefficients was used, and we also designed an elite warehouse to avoid the pitfall of conventional genetic algorithms. Then the logBB models were built as the case study. Molecular descriptors were widely used in conventional ADMET prediction methodologies. However, there are several shortcomings of using molecular descriptors. The third chapter described a chemical classification method based on substructure recognition. Therefore the molecular descriptors were avoided during the model building. In addition, information gain analysis was involved to evaluate each substructure of the molecules, which could help to interpret the machine learning models from medicinal chemistry perspective. We also presented their applications in the ADMET prediction.
Estrogen receptors (ERs) belong to nuclear receptor superfamily, which play crucial roles in human body, including the growth and differentiation of reproductive system, central nervous system and skeletal system. They are also important drug target for some diseases, including breast cancer. At present, most clinic use drugs are selective ER modulator. Although the underlying mechanism remains unclear, they have indeed alleviated the side effect of drugs. The second subtype, ERβ, which was discovered in 1996, shedding light on discovering subtype selective ligands for achieving tissue selective drugs. Therefore, many recent works focused on the differences in biological functions and pathways between the two subtypes. Besides, finding novel selective ligands could not only benefit the drug development, but also provide the chemical sensor for biological researches. The second section of this thesis is focusing on discovering selective ERβligands with various molecular modeling and CADD methods. In chapter 4, we performed steered molecular dynamics simulation to investigate the dynamic process of ligand unbinding from two different subtypes of ERs. In this work, we firstly find out that the process of ligand unbinding also contribute to the ligand selectivity. Accordingly, we proposed two suggestions for improving ERP selectivity. In chapter 5, we discovered 18 potent ligands of ERβwith virtual screening based on a structure optimized through molecular dynamics simulation. Among them, dual profile was observed in two ligands, as agonists for ERβand antagonists for ERa, which might be served as lead compounds for selective ER modulators. The results also suggest that structures optimized through MD are applicable to lead discovery. Besides, the structure activity relationship results confirmed the suggestions proposed in chapter 4. In these work, we presented an integrated work which involved theoretic calculations and experiments. The results have been verified for each other.
引文
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