Abstract
Methods of three-dimensional molecular alignment generally treat all pharmacophore features equally when superimposing. However, some pharmacophore features can be more important in a specific system. In this work, we derived the overlap volume of pharmacophore features from a molecular alignment approach as new features of molecules to build machine learning models. Features can be assigned weights to indicate their importance. With validation on DUD-E collection, models based on pharmacophore features represented by the overlap volume yielded significant performances with median AUC of approximately 0.98 and recall rate of almost 0.8.
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This research was funded by the Taishan Scholar Program of Shandong Province (tsqn201812159) and the Foundation of Clinical Pharmacy of Chinese Medical Association (LCYX-M008).
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Xiaojing Wang and Wenxiu Han are co-first authors.
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Wang, X., Han, W., Yan, X. et al. Pharmacophore features for machine learning in pharmaceutical virtual screening. Mol Divers 24, 407–412 (2020). https://doi.org/10.1007/s11030-019-09961-4
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DOI: https://doi.org/10.1007/s11030-019-09961-4