Yasuo N, Sekijima M. Improved Method of Structure-Based Virtual Screening via Interaction-Energy-Based Learning.
J Chem Inf Model 2019;
59:1050-1061. [PMID:
30808172 DOI:
10.1021/acs.jcim.8b00673]
[Citation(s) in RCA: 73] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Virtual screening is a promising method for obtaining novel hit compounds in drug discovery. It aims to enrich potentially active compounds from a large chemical library for further biological experiments. However, the accuracy of current virtual screening methods is insufficient. In this study, we develop a new virtual screening method named Similarity of Interaction Energy VEctor Score (SIEVE-Score), in which protein-ligand interaction energies are extracted to represent docking poses for machine learning. SIEVE-Score offers substantial improvements compared to other state-of-the-art virtual screening methods, namely, other machine-learning-based scoring functions, interaction fingerprints, and docking software, for the enrichment factor 1% results on the Directory of Useful Decoys, Enhanced (DUD-E). The screening results are also human-interpretable in the form of important interactions for distinguishing between active and inactive compounds. The source code is available at https://github.com/sekijima-lab/SIEVE-Score .
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