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- W4285728205 endingPage "4568" @default.
- W4285728205 startingPage "4568" @default.
- W4285728205 abstract "Molecular docking plays a significant role in early-stage drug discovery, from structure-based virtual screening (VS) to hit-to-lead optimization, and its capability and predictive power is critically dependent on the protein-ligand scoring function. In this review, we give a broad overview of recent scoring function development, as well as the docking-based applications in drug discovery. We outline the strategies and resources available for structure-based VS and discuss the assessment and development of classical and machine learning protein-ligand scoring functions. In particular, we highlight the recent progress of machine learning scoring function ranging from descriptor-based models to deep learning approaches. We also discuss the general workflow and docking protocols of structure-based VS, such as structure preparation, binding site detection, docking strategies, and post-docking filter/re-scoring, as well as a case study on the large-scale docking-based VS test on the LIT-PCBA data set." @default.
- W4285728205 created "2022-07-18" @default.
- W4285728205 creator A5001716332 @default.
- W4285728205 creator A5035730332 @default.
- W4285728205 creator A5063629758 @default.
- W4285728205 date "2022-07-18" @default.
- W4285728205 modified "2023-09-30" @default.
- W4285728205 title "Protein–Ligand Docking in the Machine-Learning Era" @default.
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