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Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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Drug-target interaction prediction: A Bayesian ranking approach.

Ladislav Peska1, Krisztian Buza2, Júlia Koller3

  • 1Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic; Brain Imaging Centre, Hungarian Academy of Sciences, Budapest, Hungary.

Computer Methods and Programs in Biomedicine
|October 22, 2017
PubMed
Summary

Bayesian Ranking Prediction of Drug-Target Interactions (BRDTI) enhances drug repositioning by predicting novel drug-target interactions. This machine learning method outperforms existing approaches, accelerating the discovery of new drug uses.

Keywords:
Bayesian personalized rankingDrug repositioningDrug-target interactionsMachine learning

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Area of Science:

  • Computational chemistry and cheminformatics.
  • Bioinformatics and computational biology.
  • Machine learning and artificial intelligence in drug discovery.

Background:

  • In silico prediction of drug-target interactions (DTI) accelerates drug repositioning.
  • Drug repositioning identifies novel uses for existing or abandoned drugs.
  • Drug-centric repositioning requires effective per-drug prediction methods.

Purpose of the Study:

  • To propose a novel machine learning method for drug-centric repositioning.
  • To develop a ranking-based approach for predicting drug-target interactions (DTI).
  • To improve upon conventional DTI prediction methods for drug repositioning.

Main Methods:

  • Bayesian Ranking Prediction of Drug-Target Interactions (BRDTI) based on Bayesian Personalized Ranking (BPR) matrix factorization.
  • Extension of BPR to incorporate target bias, handle new drugs, and utilize content alignment for structural similarities.
  • Application of machine learning to predict drug-target interactions.

Main Results:

  • BRDTI significantly outperforms state-of-the-art methods in per-drug nDCG and AUC across five benchmark datasets.
  • Achieved high nDCG scores for GPCR (0.929), IC (0.953), NR (0.948), E (0.897), and K (0.690) datasets.
  • Demonstrated ability to predict novel drug-target interactions with high recall rates.

Conclusions:

  • BRDTI is a suitable in silico DTI prediction technique for drug-centric repositioning.
  • The method effectively identifies potential new uses for existing drugs.
  • BRDTI software and supplementary materials are publicly available.