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[Display omitted] • New machine-learning scoring functions were developed to predict PD1/PDL1 inhibitors. • PDL1-specific regressors exploiting large volumes of inactive-enriched data and PLEC fingerprints as features were the most predictive for this important target. • These new scoring functions are made available free of charge for prospective use. Small-molecule Programmable Cell Death Protein 1/Programmable Death-Ligand 1 (PD1/PDL1) inhibition via PDL1 dimerization has the potential to lead to inexpensive drugs with better cancer patient outcomes and milder side effects. However, this therapeutic approach has proven challenging, with only one PDL1 dimerizer reaching early clinical trials so far. There is hence a need for fast and accurate methods to develop alternative PDL1 dimerizers. We aim to show that structure-based virtual screening (SBVS) based on PDL1-specific machine-learning (ML) scoring functions (SFs) is a powerful drug design tool for detecting PD1/PDL1 inhibitors via PDL1 dimerization. By incorporating the latest MLSF advances, we generated and evaluated PDL1-specific MLSFs (classifiers and inactive-enriched regressors) on two demanding test sets. 60 PDL1-specific MLSFs (30 classifiers and 30 regressors) were generated. Our large-scale analysis provides highly predictive PDL1-specific MLSFs that benefitted from training with large volumes of docked inactives and enabling inactive-enriched regression. PDL1-specific MLSFs strongly outperformed generic SFs of various types on this target and are released here without restrictions. [ABSTRACT FROM AUTHOR]

Larger training datasets have been shown to improve the accuracy of machine learning (ML)-based scoring functions (SFs) for structure-based virtual screening (SBVS). In addition, massive test sets for SBVS, known as ultra-large compound libraries, have been demonstrated to enable the fast discovery of selective drug leads with low-nanomolar potency. This proof-of-concept was carried out on two targets using a single docking tool along with its SF. It is thus unclear whether this high level of performance would generalise to other targets, docking tools and SFs. We found that screening a larger compound library results in more potent actives being identified in all six additional targets using a different docking tool along with its classical SF. Furthermore, we established that a way to improve the potency of the retrieved molecules further is to rank them with more accurate ML-based SFs (we found this to be true in four of the six targets; the difference was not significant in the remaining two targets). A 3-fold increase in average hit rate across targets was also achieved by the ML-based SFs. Lastly, we observed that classical and ML-based SFs often find different actives, which supports using both types of SFs on those targets. [ABSTRACT FROM AUTHOR]

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 [ABSTRACT FROM AUTHOR]

Stochastic computing is an emerging scientific field pushed by the need for developing high-performance artificial intelligence systems in hardware to quickly solve complex data processing problems. This is the case of virtual screening, a computational task aimed at searching across huge molecular databases for new drug leads. In this work, we show a classification framework in which molecules are described by an energy-based vector. This vector is then processed by an ultra-fast artificial neural network implemented through FPGA by using stochastic computing techniques. Compared to other previously published virtual screening methods, this proposal provides similar or higher accuracy, while it improves processing speed by about two or three orders of magnitude. [ABSTRACT FROM AUTHOR]