Virtual Screening System for Finding Structurally Diverse Hits by Active Learning

Abstract

Two virtual screening strategies, "query by bagging" (QBag) and "query by bagging with descriptor-sampling" (QBagDS), based on active learning were devised. The QBag strategy generates multiple structure-activity relationship rules by bagging and selects compounds to improve the rules. To find many structurally diverse hits, the QBagDS strategy generates rules by bagging with descriptor sampling. They can also use prior knowledge about hits to improve the efficiency at the beginning of screening. We performed simulation experiments and clustering analysis for several G-protein coupled receptors and showed that the QBag and QBagDS strategies outperform the conventional similarity-based strategy and that using both descriptor sampling and prior knowledge are effective for finding many hits. We applied the bagging with descriptor sampling strategy to novel hit finding, and 4 of the 10 selected compounds showed high inhibition.

Iterative Refinement of a Binding Pocket Model: Active Computational Steering of Lead Optimization

“…The focus was on activity alone and did not assess questions of structural diversity. Fujiwara et al 18 studied active learning in the context of virtual screening and considered the question of structural diversity. As with the Warmuth study, compound activity was considered as a binary variable and temporal considerations were not taken into account.…”

Multi-objective active machine learning rapidly improves structure–activity models and reveals new protein–protein interaction inhibitors

“…Thereby, the best use is made of the bioactivity data, while limiting the overall number of assays performed. 7 New compounds are either selected with a focus on maximal information content and diversity of the molecular reference structures (explorative strategy) 8 , 9 or with a focus on improved bioactivity (exploitive/greedy strategy). 10 – 12 Until now, this concept has essentially been studied only theoretically.…”

“…Furthermore, we propose a technique for the informed batch-wise selection of compounds, which is of particular practical relevance for the application of active learning in the context of biological studies where many assays are effectively performed in batches. 9 , 13 …”

Efficient discovery of responses of proteins to compounds using active learning

“…There have been limited previous applications of active learning to the drug discovery process. In these efforts, compound activity was considered to be binary (active or inactive) and effort was focused on only a single target [22,23]. …”