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Prediction of compound-target interaction using several artificial intelligence algorithms and comparison with a

Karina Jimenes-Vargas1,2, Alejandro Pazos3,4,5, Cristian R Munteanu3,4,5

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Predicting protein targets is vital for drug discovery. This study shows that developed target-centric models (TCM) outperform existing web tools, especially when using a consensus strategy for improved accuracy.

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Ligan-based modelingMachine learningQSARTarget fishingTarget identification

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Area of Science:

  • Computational chemistry
  • Bioinformatics
  • Drug discovery

Background:

  • Accurate prediction of protein targets for chemical compounds is essential for understanding mechanisms of action, side effects, and advancing drug discovery.
  • Existing computational models for compound-target interaction prediction vary in performance.

Purpose of the Study:

  • To develop and evaluate novel target-centric models (TCM) for predicting compound-protein interactions.
  • To compare the performance of in-house developed TCMs against publicly available web tools (WTCM).
  • To investigate the efficacy of consensus strategies in improving prediction accuracy.

Main Methods:

  • Development of 15 target-centric models (TCM) using diverse molecular descriptions and machine learning algorithms.
  • Evaluation and comparison with 17 third-party web tools (WTCM).
  • Implementation and assessment of consensus strategies for aggregating predictions.

Main Results:

  • Developed TCMs achieved f1-score values greater than 0.8.
  • The best TCM outperformed the best WTCM across key metrics (TPR, TNR, FNR, FPR).
  • Consensus strategies significantly improved prediction relevance, with TCM consensus reaching 0.98 TPR and 0 FNR.

Conclusions:

  • Target-centric models, particularly when employing a consensus strategy, offer superior performance for compound-target interaction prediction compared to existing web tools.
  • The developed computational tool and consensus approach provide a valuable resource for drug discovery and chemical biology research.