{"ID":2855732,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.12445","arxiv_id":"2510.12445","title":"M3ST-DTI: A multi-task learning model for drug-target interactions based on multi-modal features and multi-stage alignment","abstract":"Accurate prediction of drug-target interactions (DTI) is pivotal in drug discovery. However, existing approaches often fail to capture deep intra-modal feature interactions or achieve effective cross-modal alignment, limiting predictive performance and generalization. To address these challenges, we propose M3ST-DTI, a multi-task learning model that enables multi-stage integration and alignment of multi modal features for DTI prediction. M3ST-DTI incorporates three types of features-textual, structural, and functional and enhances intra-modal representations using self-attention mechanisms and a hybrid pooling graph attention module. For early-stage feature alignment and fusion, the model in tegrates MCA with Gram loss as a structural constraint. In the later stage, a BCA module captures fine-grained interactions between drugs and targets within each modality, while a deep orthogonal fusion module mitigates feature redundancy.Extensive evaluations on benchmark datasets demonstrate that M3ST-DTI consistently outperforms state-of-the art methods across diverse metrics","short_abstract":"Accurate prediction of drug-target interactions (DTI) is pivotal in drug discovery. However, existing approaches often fail to capture deep intra-modal feature interactions or achieve effective cross-modal alignment, limiting predictive performance and generalization. To address these challenges, we propose M3ST-DTI, a...","url_abs":"https://arxiv.org/abs/2510.12445","url_pdf":"https://arxiv.org/pdf/2510.12445v2","authors":"[\"Xiangyu Li\",\"Ran Su\",\"Liangliang Liu\"]","published":"2025-10-14T12:26:58Z","proceeding":"cs.MM","tasks":"[\"cs.MM\"]","methods":"[]","has_code":false}