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DrugProtAI: A machine learning-driven approach for predicting protein druggability through feature engineering and

Ankit Halder1, Sabyasachi Samantaray2, Sahil Barbade3

  • 1Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, Maharashtra, India.

Briefings in Bioinformatics
|July 8, 2025
PubMed
Summary

DrugProtAI enhances drug discovery by predicting protein druggability using machine learning on the complete human proteome. This computational tool improves target selection accuracy, aiming to reduce drug development failures.

Keywords:
drug discoverydruggable targetsensemble-based methodsfeature selectionmachine learning

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Pharmacology

Background:

  • Drug development faces high failure rates, often due to poor target selection.
  • Current computational methods for predicting protein druggability are limited in scope and features.
  • Precision and sensitivity in identifying viable drug targets are critical for successful clinical research.

Purpose of the Study:

  • To develop and validate DrugProtAI, a novel computational tool for predicting human protein druggability.
  • To improve the accuracy and scope of drug target identification in early-stage drug discovery.
  • To provide a freely accessible resource for researchers to assess protein druggability.

Main Methods:

  • Implemented a partitioning-based method trained on the entire human proteome.
  • Utilized a comprehensive feature set including 183 biophysical, sequence- and non-sequence-derived properties.
  • Evaluated machine learning algorithms, with Random Forest and XGBoost showing optimal performance.

Main Results:

  • Achieved a median Area Under Precision-Recall Curve (AUC) of 0.87 for target prediction.
  • Validated the model on a blinded dataset of recently approved drug targets.
  • Identified key predictors contributing to protein druggability, aiding target selection.

Conclusions:

  • DrugProtAI offers a significant advancement in predicting protein druggability for human proteins.
  • The tool's comprehensive approach and high performance can help mitigate drug development failures.
  • DrugProtAI is accessible online, empowering researchers with data-driven target selection capabilities.