A Molecular Representation Learning Model Based on Multidimensional Joint and Cross-Learning for Drug-Drug Interaction Prediction.

Journal: Journal of chemical information and modeling
Published Date: Aug 4, 2025

Abstract

Drug-drug interactions (DDIs) present significant challenges within clinical pharmacology, as they can impact therapeutic outcomes, especially given the growing prevalence of polypharmacy. Traditional methods for the clinical validation of DDIs typically exhibit inefficiency and high cost, underscoring the necessity for more advanced computational methodologies. Although deep learning-based methods have improved DDI prediction performance, current approaches often face challenges in extracting and integrating multidimensional molecular features and capturing molecular reaction patterns. To overcome these limitations, we propose a Multidimensional Joint and Cross-learning (MDJCL) model that effectively integrates 1D, 2D, and 3D molecular features of drugs. A cross-attention fusion module aggregates multidimensional features while minimizing information loss, and a molecular-pair reaction module pinpoints potential interaction sites. Experimental results on benchmark data sets demonstrate that MDJCL consistently outperforms state-of-the-art models. Ablation studies reveal that each module contributes distinctively to the overall enhancement of evaluation metrics. These results validate the effectiveness of multidimensional feature integration and cross learning mechanisms in enhancing DDI prediction, offering a reliable tool for clinical decision-making and precision medicine.

Authors

  • Congzhou Chen
    College of Information Science and Technology, Beijing University of Chemical Technology, Beijing100029, China.
  • Xingyu Shi
    College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China.
  • Jinyan Nie
    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing100094, China.
  • Jin Xu
    Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, and School of Statistics, East China Normal University, Shanghai, China.
  • Lingfeng Wang
    School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China.

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