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- W2010521808 abstract "Virtual screening based on protein-ligand docking is widely applied at the early stage of drug discovery. Scoring functions from a diverse set of existing protein-ligand docking tools, however, often poorly distinguish bioactive compounds from inactive ones. As a result, considerable effort has been devoted to the combination of multiple scoring functions for more reliable evaluation. State-of-the-art consensus scoring or ensemble learning methods assume each scoring function performs uniformly for all cases. Case-based meta-learning (CBML), the method we have developed, is fundamentally different. It identifies the best predictor for a specific new case based on its similarity to old cases and uses that method to predict rather than average the performance of all predictors. Our large-scale benchmark studies clearly indicate that CBML outperforms consensus-based scoring and significantly improves the performance of structure-based virtual screening. The CBML paradigm can be extended to other applications in bioinformatics and chemoinformatics for robust and reliable predictive modeling." @default.
- W2010521808 created "2016-06-24" @default.
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- W2010521808 date "2013-12-01" @default.
- W2010521808 modified "2023-09-27" @default.
- W2010521808 title "A case-based meta-learning algorithm boosts the performance of structure-based virtual screening" @default.
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- W2010521808 doi "https://doi.org/10.1109/bibm.2013.6732464" @default.
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