A Bayesian machine learning approach for drug target identification using diverse data types
By Neel S. Madhukar, Prashant K. Khade, Linda Huang, Kaitlyn M. Gayvert, G. Galletti, M. Stogniew, J. Allen, P. Giannakakou, Olivier Elemento. 2019
197 citations recorded by the source corpus.
Drug target identification is a crucial step in development, yet is also among the most complex. To address this, we develop BANDIT, a Bayesian machine-learning approach that integrates multiple data types to predict drug binding targets. Integrating public data, BANDIT benchmarked a ~90% accuracy on 2000+ small molecules. Applied to 14,000+ compounds without known targets, BANDIT generated ~4,000 previously unknown molecule-target predictions. From this set we validate 14 novel microtubule inhibitors, including 3 with activity on resistant cancer cells. We applied BANDIT to ONC201—an anti-cancer compound in clinical development whose target had remained elusive. We identified and validated DRD2 as ONC201’s target, and this information is now being used for precise clinical trial design. Finally, BANDIT identifies connections between different drug classes, elucidating previously unexplained clinical observations and suggesting new drug repositioning opportunities. Overall, BANDIT represents an efficient and accurate platform to accelerate drug discovery and direct clinical application. Drug target identification is a crucial step in drug development. Here, the authors introduce a Bayesian machine learning framework that integrates multiple data types to predict the targets of small molecules, enabling identification of a new set of microtubule inhibitors and the target of the anti-cancer molecule ONC201.
Subfields: Computational Theory and Mathematics, Molecular Biology
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- Carlos Fernandez-Lozano
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- Paula Carracedo-Reboredo
- Victor Maojo
- Francisco Cedron
- Adrián Carballal
Citations in the corpus
- A review on machine learning approaches and trends in drug discovery (2021, background): “Specifically, in [123] they developed a Bayesian model that integrates different data sources such as known side effects, or gene expression data, achieving a model with 90% accuracy on more than 2,000 molecules and also developing the experimental validation of the screening process.”