Machine‐learning scoring functions for structure‐based virtual screening

Abstract 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