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- W2093876128 abstract "Virtual screening is an effective way to find hits in drug discovery, with approaches ranging from fast information-based similarity methods to more computationally intensive physics-based docking methods. However, the best approach to use for a given project is not clear in advance of the screen. In this work, we show that combining results from multiple methods using a standard score (Z-score) can significantly improve virtual screening enrichments over any of the single screening methods. We show that an augmented Z-score, which considers the best two out of three scores for a given compound, outperforms previously published data fusion algorithms. We use three different virtual screening methods (two-dimensional (2D) fingerprint similarity, shape-based similarity, and docking) and study two different databases (DUD and MDDR). The average enrichment in the top 1% was improved by 9% for DUD and 25% for the MDDR, compared with the top individual method. Improvements of 22% for DUD and 43% for MDDR are seen over the average of the three individual methods. Statistics are presented that show a high significance associated with the findings in this work." @default.
- W2093876128 created "2016-06-24" @default.
- W2093876128 creator A5058404949 @default.
- W2093876128 creator A5087455853 @default.
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- W2093876128 date "2013-07-03" @default.
- W2093876128 modified "2023-10-03" @default.
- W2093876128 title "Boosting Virtual Screening Enrichments with Data Fusion: Coalescing Hits from Two-Dimensional Fingerprints, Shape, and Docking" @default.
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- W2093876128 doi "https://doi.org/10.1021/ci300463g" @default.
- W2093876128 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/23782297" @default.
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