From machine learning to deep learning: Advances in scoring functions for protein–ligand docking
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- Chao Shen
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University Hangzhou P. R. China
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- Junjie Ding
- Beijing Institute of Pharmaceutical Chemistry Beijing P. R. China
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- Zhe Wang
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University Hangzhou P. R. China
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- Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University Changsha P. R. China
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- Xiaoqin Ding
- Beijing Institute of Pharmaceutical Chemistry Beijing P. R. China
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- Tingjun Hou
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University Hangzhou P. R. China
説明
<jats:title>Abstract</jats:title><jats:p>Molecule docking has been regarded as a routine tool for drug discovery, but its accuracy highly depends on the reliability of scoring functions (SFs). With the rapid development of machine learning (ML) techniques, ML‐based SFs have gradually emerged as a promising alternative for protein–ligand binding affinity prediction and virtual screening, and most of them have shown significantly better performance than a wide range of classical SFs. Emergence of more data‐hungry deep learning (DL) approaches in recent years further fascinates the exploitation of more accurate SFs. Here, we summarize the progress of traditional ML‐based SFs in the last few years and provide insights into recently developed DL‐based SFs. We believe that the continuous improvement in ML‐based SFs can surely guide the early‐stage drug design and accelerate the discovery of new drugs.</jats:p><jats:p>This article is categorized under: <jats:list list-type="simple"> <jats:list-item><jats:p>Computer and Information Science > Chemoinformatics</jats:p></jats:list-item> </jats:list></jats:p>
収録刊行物
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- WIREs Computational Molecular Science
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WIREs Computational Molecular Science 10 (1), 2019-06-27
Wiley
