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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
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A Bayesian machine learning approach for drug target identification using diverse data types.

Neel S Madhukar1,2,3,4,5, Prashant K Khade6, Linda Huang1,2,3

  • 1Institute for Computational Biomedicine, Dept. of Physiology and Biophysics, Weill Cornell Medical College, New York, NY, 10065, USA.

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We developed BANDIT, a machine-learning tool for drug target identification. It accurately predicts drug targets, accelerating drug discovery and enabling new therapeutic strategies.

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Area of Science:

  • Computational biology
  • Pharmacology
  • Machine learning

Background:

  • Drug target identification is a complex and critical bottleneck in drug development.
  • Existing methods often lack the capacity to integrate diverse data types for accurate prediction.

Purpose of the Study:

  • To develop and validate BANDIT, a Bayesian machine-learning approach for predicting drug binding targets.
  • To accelerate drug discovery by identifying novel molecule-target interactions and drug repositioning opportunities.

Main Methods:

  • BANDIT integrates multiple data types using a Bayesian machine-learning framework.
  • The approach was benchmarked on over 2000 small molecules using public data, achieving approximately 90% accuracy.
  • It was applied to over 14,000 compounds with unknown targets.

Main Results:

  • BANDIT generated over 4,000 novel molecule-target predictions.
  • 14 novel microtubule inhibitors were validated, including 3 effective against resistant cancer cells.
  • The target of the anti-cancer compound ONC201 was identified as DRD2, informing clinical trial design.
  • New connections between drug classes were identified, suggesting drug repositioning opportunities.

Conclusions:

  • BANDIT is an efficient and accurate platform for accelerating drug discovery and development.
  • The tool facilitates precise clinical trial design and identifies novel therapeutic strategies.
  • BANDIT aids in understanding drug class interactions and uncovering drug repositioning potential.