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

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The proposed model enhances drug-drug interaction prediction using multidimensional features, suggesting better clinical outcomes.

The MDJCL model significantly enhances drug-drug interaction prediction and improves clinical pharmacology outcomes.
Experimental results reveal that the model outperformed state-of-the-art methods on benchmark data sets, showcasing significant advancements.
Using a computational approach, the model integrates 1D, 2D, and 3D molecular features effectively for accurate prediction.
The results validate the effectiveness of cross-learning and multidimensional feature integration in enhancing DDI predictions.

Chen et al. (2025) studied this question.

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