Efficient discovery of responses of proteins to compounds using active learning

Abstract

BackgroundDrug discovery and development has been aided by high throughput screening methods that detect compound effects on a single target. However, when using focused initial screening, undesirable secondary effects are often detected late in the development process after significant investment has been made. An alternative approach would be to screen against undesired effects early in the process, but the number of possible secondary targets makes this prohibitively expensive.ResultsThis paper describes methods for making this global approach practical by constructing predictive models for many target responses to many compounds and using them to guide experimentation. We demonstrate for the first time that by jointly modeling targets and compounds using descriptive features and using active machine learning methods, accurate models can be built by doing only a small fraction of possible experiments. The methods were evaluated by computational experiments using a dataset of 177 assays and 20,000 compounds constructed from the PubChem database.ConclusionsAn average of nearly 60% of all hits in the dataset were found after exploring only 3% of the experimental space which suggests that active learning can be used to enable more complete characterization of compound effects than otherwise affordable. The methods described are also likely to find widespread application outside drug discovery, such as for characterizing the effects of a large number of compounds or inhibitory RNAs on a large number of cell or tissue phenotypes.

Active Learning for Computational Chemogenomics

“…In other words, active learning can successfully navigate different projections of ligand-receptor spaces. Consistent with previous applications of active learning [58], we could show that family-dependent sequence similarities captured by the dipeptide descriptors can yield predictive chemogenomics models. However, applications that study the subtle pharmacology between similar proteins or multiple binding pockets might require alternative, more detailed descriptors that capture the structure of protein cavities relevant to ligand binding.…”

“…It was previously shown that active learning could query separate ligand-or target-based models to aid in improving the understanding of polypharmacological networks [58]. We have extended this hypothesis using chemogenomic modeling to capture the combined ligand-target space and aim at extrapolating knowledge from the interaction patterns.…”

“…That is, we investigate the potential of active learning to act as the steering wheel for family-wide computational chemogenomic model building. We evaluate its ability to predict bioactivity and how it evolves a chemogenomic model [49,[56][57][58], finding that protein/target family-wide chemogenomic active learning can build an interaction model from only a small fraction of bioactivity data points in a screening database. These models show high predictive performance on datasets many folds larger than that used for model construction.…”

Small Random Forest Models for Effective Chemogenomic Active Learning

“…In the area of drug discovery, active learning has been shown to efficiently derive high-performance prediction models based on small subsets of input data [32,34,39,46]. Furthermore, actively trained models not only reached significantly higher hit rates compared to experimental standards which frequently remain below 1 % in cases of unbiased chemical libraries [34,39,40,[47][48][49], but such models also contributed to successful identification of novel bioactive compounds [33,36,42] and cancer rescue mutants of p53 [31]. Overall, AL approaches bear the potential to improve drug discovery processes by increasing hit rates, reducing the amount of time-and cost-intensive experimentation, and accelerate hit-to-lead processes through integration into a feedback-driven experimentation workflow [33,36,42,43,50].…”

“…To date, several pro-and retrospective studies report successful applications of AL strategies throughout different fields of research [27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46]. In the area of drug discovery, active learning has been shown to efficiently derive high-performance prediction models based on small subsets of input data [32,34,39,46].…”