A Bayesian machine learning approach for drug target identification using diverse data types.
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ABSTRACT:
SUBMITTER: Madhukar NS
PROVIDER: S-EPMC6863850 | biostudies-literature | 2019 Nov
REPOSITORIES: biostudies-literature
Publications
A Bayesian machine learning approach for drug target identification using diverse data types.
Madhukar Neel S NS Khade Prashant K PK Huang Linda L Gayvert Kaitlyn K Galletti Giuseppe G Stogniew Martin M Allen Joshua E JE Giannakakou Paraskevi P Elemento Olivier O
Nature communications 20191119 1
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 inhi ...[more]
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