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Machine-learning scoring functions for structure-based virtual screening
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DOI：10.1002/wcms.1478.png)
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Abstract
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Molecular docking predicts whether and how small molecules bind to a macromolecular target using a suitable 3D structure. Scoring functions for structure-based virtual screening primarily aim at discovering which molecules bind to the considered target when these form part of a library with a much higher proportion of non-binders. Classical scoring functions are essentially models building a linear mapping between the features describing a protein-ligand complex and its binding label. Machine learning, a major subfield of artificial intelligence, can also be used to build fast supervised learning models for this task. In this review, we analyzed such machine-learning scoring functions for structure-based virtual screening in the period 2015-2019. We have discussed what the shortcomings of current benchmarks really mean and what valid alternatives have been employed. The latter retrospective studies observed that machine-learning scoring functions were substantially more accurate, in terms of higher hit rates and potencies, than the classical scoring functions they were compared to. Several of these machine-learning scoring functions were also employed in prospective studies, in which mid-nanomolar binders with novel chemical structures were directly discovered without any potency optimization. We have thus highlighted the codes and webservers that are available to build or apply machine-learning scoring functions to prospective structure-based virtual screening studies. A discussion of prospects for future work completes this review. This article is categorized under: Computer and Information Science > Chemoinformatics
Keywords:
artificial intelligence
machine learning
molecular docking
scoring function
virtual screening
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