Unbiasing Scoring Functions: A New Normalization and Rescoring Strategy
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- 作者：Giorgio Carta ; Andrew J. S. Knox ; David G. Lloyd
- 刊名：Journal of Chemical Information and Modeling
- 出版年：2007
- 出版时间：July 2007
- 年：2007
- 卷：47
- 期：4
- 页码：1564 - 1571
- 全文大小：657K
- 年卷期：v.47,no.4(July 2007)
- ISSN：1549-960X
文摘
Ligand bias can contribute significantly to the number of false positives observed in a virtual screeningcampaign. Using a receptor-based docking approach against a well-established target of therapeutic importance,estrogen receptor 
s/gifchars/alpha.gif" BORDER=0> (ERs/gifchars/alpha.gif" BORDER=0>), coupled with several common scoring functions (ChemGuass, ChemGauss2,ChemScore, ScreenScore, ShapeGauss, and PLP), taken both individually and as a consensus, we sought toexamine the characteristics of molecules retrieved by each. It has been previously shown that scoring functions(mainly empirical) exhibit bias in prioritizing more complicated molecules arising from additive componentswithin the function. Using Spearmen's correlation coefficient, we show that a large set of descriptors calculatedfor a docked set of molecules exhibit positive correlation with the ranked position in a hitlist. Moreover,most of these descriptors correlate well with MW. To this end, rather than penalizing the docked score ofall heavy molecular weight (MW) molecules and rewarding those of lower MW, as is common practice, weexamine the impact of penalizing the score only of those molecules which were of higher MW, leavinglower MW molecules unaffected. Here, we introduce a new power function to aid the process. Using scoringfrequency analysis and SIFt fingerprints, we acheived a more meaningful analysis of virtual screening (VS)performance than with enrichment calculations, facilitating target-specific VS method development.