A Unified, Probabilistic Framework for Structure- and Ligand-Based Virtual Screening

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• 作者：Steven L. Swann ; Scott P. Brown ; Steven W. Muchmore ; Hetal Patel ; Philip Merta ; John Locklear ; Philip J. Hajduk
• 刊名：Journal of Medicinal Chemistry
• 出版年：2011
• 出版时间：March 10, 2011
• 年：2011
• 卷：54
• 期：5
• 页码：1223-1232
• 全文大小：1001K
• 年卷期：v.54,no.5(March 10, 2011)
• ISSN：1520-4804

文摘

We present a probabilistic framework for interpreting structure-based virtual screening that returns a quantitative likelihood of observing bioactivity and can be quantitatively combined with ligand-based screening methods to yield a cumulative prediction that consistently outperforms any single screening metric. The approach has been developed and validated on more than 30 different protein targets. Transforming structure-based in silico screening results into robust probabilities of activity enables the general fusion of multiple structure- and ligand-based approaches and returns a quantitative expectation of success that can be used to prioritize (or deprioritize) further discovery activities. This unified probabilistic framework offers a paradigm shift in how docking and scoring results are interpreted, which can enhance early lead-finding efforts by maximizing the value of in silico computational tools.