Learning from the ligand: using ligand-based features to improve binding affinity prediction

Learning from the ligand: using ligand-based features to improve binding affinity prediction

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Machine learning scoring functions for protein-ligand binding affinity prediction have been found to consistently outperform classical scoring functions. Structure-based scoring functions for universal affinity prediction typically use features describing interactions derived from the protein-ligand...

書誌詳細
主要な著者:	Boyles, F, Deane, C, Morris, G
フォーマット:	Journal article
出版事項:	2019

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