Virtual Screening Data Fusion Using Both Structure- and Ligand-Based Methods

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• 作者：Fredrik Svensson ; Anders Karl茅n ; Christian Sk枚ld
• 刊名：Journal of Chemical Information and Modeling
• 出版年：2012
• 出版时间：January 23, 2012
• 年：2012
• 卷：52
• 期：1
• 页码：225-232
• 全文大小：451K
• 年卷期：v.52,no.1(January 23, 2012)
• ISSN：1549-960X

文摘

Virtual screening is widely applied in drug discovery, and significant effort has been put into improving current methods. In this study, we have evaluated the performance of compound ranking in virtual screening using five different data fusion algorithms on a total of 16 data sets. The data were generated by docking, pharmacophore search, shape similarity, and electrostatic similarity, spanning both structure- and ligand-based methods. The algorithms used for data fusion were sum rank, rank vote, sum score, Pareto ranking, and parallel selection. None of the fusion methods require any prior knowledge or input other than the results from the single methods and, thus, are readily applicable. The results show that compound ranking using data fusion improves the performance and consistency of virtual screening compared to the single methods alone. The best performing data fusion algorithm was parallel selection, but both rank voting and Pareto ranking also have good performance.