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Open Access Highly Accessed Research article

Efficient discovery of responses of proteins to compounds using active learning

Joshua D Kangas1, Armaghan W Naik1 and Robert F Murphy123*

Author Affiliations

1 Lane Center for Computational Biology, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA 15213, USA

2 Departments of Biological Sciences, Machine Learning and Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA

3 Freiburg Institute for Advanced Studies and Faculty of Biology, Albert Ludwig University, Freiburg, Germany

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BMC Bioinformatics 2014, 15:143  doi:10.1186/1471-2105-15-143

Published: 16 May 2014

Abstract

Background

Drug 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.

Results

This 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.

Conclusions

An 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.

Keywords:
Active learning; Machine learning; Drug development; Polypharmacology; Computational biology; Drug discovery