Journal
WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE
Volume 10, Issue 1, Pages -Publisher
WILEY
DOI: 10.1002/wcms.1429
Keywords
deep learning; machine learning; molecular docking; scoring function; structure-based drug design
Funding
- National Key R&D Program of China [2016YFA0501701, 2016YFB0201700]
- National Natural Science Foundation of China [21575128, 81603031, 81773632]
- Zhejiang Provincial Natural Science Foundation of China [LZ19H300001]
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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. This article is categorized under: Computer and Information Science > Chemoinformatics
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