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TriMod-DTI, a novel triple-modal contrastive learning framework, was developed to improve drug-target interaction (DTI) prediction by integrating 1D sequence, 2D graph, and 3D structural information of drugs and proteins. A feature extractor captures drug and target features across these modalities, and a contrastive learning strategy aligns representations in the latent space. Experiments on benchmark datasets demonstrate that TriMod-DTI outperforms state-of-the-art methods, highlighting the importance of multi-modal integration for DTI prediction.
Integrating 3D structural features with sequence and graph representations in a contrastive learning framework significantly boosts drug-target interaction prediction accuracy.
Accurate prediction of drug-target interactions (DTI) is critical for drug discovery. Existing methods often rely on single-modal representations (e.g., sequences or graphs) or combine only two modalities, overlooking 3D structural features. To address this challenge, we propose TriMod-DTI, a triple-modal contrastive learning framework that incorporates 1D sequences, 2D graphs, and 3D structures of drugs and proteins, obtaining the universal and complementary feature representations for DTI prediction. We design a Feature Extractor to capture drug and target features across the three modalities, thereby enriching their representations. We further propose a triple-modal contrastive learning strategy to align different modal representations of the same drug or protein in the latent space. By constructing cross-modal positive and negative sample pairs, this approach enhances the model's discriminative ability. Experiments on three benchmark datasets demonstrate that TriMod-DTI outperforms state-of-the-art methods. The ablation studies validate the contributions of each modality. Moreover, case studies highlight its practical potential for DTI prediction and drug discovery.
