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<p>With the advantages of reducing biochemical experiments and enabling the rapid screening of potential druggable compounds, accurate computational methods are essential for predicting Drug-Target affinity (DTA). Current deep learning-based DTA prediction methods predominantly concentrate on single-modal information from drugs or targets. In this article, we propose a new multi-modal DTA prediction method, MGSDTA, to integrate graph features and sequence features of drug molecules and target proteins. We extract features from the drug molecular graphs and target protein graphs, meanwhile,&#xa0;we&#xa0;extract sequence features&#xa0;using&#xa0;continuous embeddings generated by advanced self-supervised pre-trained&#xa0;models,&#xa0;Mol2vec and&#xa0;ProtVec,&#xa0;for drug substructures and target&#xa0;subsequences respectively. Finally, they are integrated&#xa0;with a weighted fusion module for DTA prediction. Experiments on benchmark datasets indicate that the performance of MGSDTA exceeds single-modal methods based solely on sequences or graphs.</p> Graphical Abstract <p></p>

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A Multi-modal Drug Target Affinity Prediction Based on Graph Features and Pre-trained Sequence Embeddings

  • 期刊论文

  • 发表日期：

    2025年6月2日

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  • Xin Tang,
  • Xiujuan Lei,
  • Lian Liu

摘要

With the advantages of reducing biochemical experiments and enabling the rapid screening of potential druggable compounds, accurate computational methods are essential for predicting Drug-Target affinity (DTA). Current deep learning-based DTA prediction methods predominantly concentrate on single-modal information from drugs or targets. In this article, we propose a new multi-modal DTA prediction method, MGSDTA, to integrate graph features and sequence features of drug molecules and target proteins. We extract features from the drug molecular graphs and target protein graphs, meanwhile, we extract sequence features using continuous embeddings generated by advanced self-supervised pre-trained models, Mol2vec and ProtVec, for drug substructures and target subsequences respectively. Finally, they are integrated with a weighted fusion module for DTA prediction. Experiments on benchmark datasets indicate that the performance of MGSDTA exceeds single-modal methods based solely on sequences or graphs.

Graphical Abstract