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GeneralizedDTA: combining pre-training and multi-task learning to predict drug-target binding affinity for unknown

Shaofu Lin1, Chengyu Shi1, Jianhui Chen2,3,4

  • 1Faculty of Information Technology, Beijing University of Technology, No. 100, Pingleyuan, Chaoyang District, Beijing, 100124, China.

BMC Bioinformatics
|September 7, 2022
PubMed
Summary

This study introduces GeneralizedDTA, a novel model for predicting drug-target binding affinity (DTA) in silico. GeneralizedDTA enhances predictions for unknown drugs by combining self-supervised pre-training with multi-task learning.

Keywords:
DTA predictionDual adaptation mechanismMulti-task learningPre-training task

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Area of Science:

  • Computational chemistry
  • Bioinformatics
  • Drug discovery

Background:

  • Accurate in silico prediction of drug-target binding affinity (DTA) is crucial for drug discovery.
  • Current machine learning models, particularly deep neural networks, struggle with limited labeled data and out-of-distribution challenges for novel compounds.
  • Existing methods face catastrophic forgetting due to the task gap between self-supervised pre-training and DTA prediction.

Purpose of the Study:

  • To develop a novel DTA prediction model, GeneralizedDTA, specifically designed for unknown drug discovery.
  • To enhance feature representation and accelerate model convergence using self-supervised pre-training for both proteins and drugs.
  • To mitigate overfitting and improve generalization for unknown drug targets through a multi-task learning framework with dual adaptation.

Main Methods:

  • Implemented self-supervised pre-training tasks for protein amino acid sequences and drug molecular graphs to learn rich structural information.
  • Developed a multi-task learning framework incorporating a dual adaptation mechanism to bridge the gap between pre-training and DTA prediction.
  • Constructed a dedicated unknown drug dataset to simulate and evaluate performance in a realistic drug discovery scenario.

Main Results:

  • GeneralizedDTA demonstrated superior generalization capability in predicting DTA for unknown drugs compared to existing models.
  • The model effectively alleviated high variance issues in deep neural network encodings.
  • Accelerated convergence of the prediction model was observed, even with small-scale labeled data.

Conclusions:

  • The proposed GeneralizedDTA model effectively leverages pre-training tasks and a multi-task learning framework.
  • Richer structural information from large-scale unlabeled protein and drug data is learned and integrated into downstream prediction.
  • The approach yields high-quality DTA predictions, significantly improving drug discovery for unknown compounds.