{"ID":2844302,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.06269","arxiv_id":"2511.06269","title":"LLM$^3$-DTI: A Large Language Model and Multi-modal data co-powered framework for Drug-Target Interaction prediction","abstract":"Drug-target interaction (DTI) prediction is of great significance for drug discovery and drug repurposing. With the accumulation of a large volume of valuable data, data-driven methods have been increasingly harnessed to predict DTIs, reducing costs across various dimensions. Therefore, this paper proposes a $\\textbf{L}$arge $\\textbf{L}$anguage $\\textbf{M}$odel and $\\textbf{M}$ulti-$\\textbf{M}$odel data co-powered $\\textbf{D}$rug $\\textbf{T}$arget $\\textbf{I}$nteraction prediction framework, named LLM$^3$-DTI. LLM$^3$-DTI constructs multi-modal data embedding to enhance DTI prediction performance. In this framework, the text semantic embeddings of drugs and targets are encoded by a domain-specific LLM. To effectively align and fuse multi-modal embedding. We propose the dual cross-attention mechanism and the TSFusion module. Finally, these multi-modal data are utilized for the DTI task through an output network. The experimental results indicate that LLM$^3$-DTI can proficiently identify validated DTIs, surpassing the performance of the models employed for comparison across diverse scenarios. Consequently, LLM$^3$-DTI is adept at fulfilling the task of DTI prediction with excellence. The data and code are available at https://github.com/chaser-gua/LLM3DTI.","short_abstract":"Drug-target interaction (DTI) prediction is of great significance for drug discovery and drug repurposing. With the accumulation of a large volume of valuable data, data-driven methods have been increasingly harnessed to predict DTIs, reducing costs across various dimensions. Therefore, this paper proposes a $\\textbf{L...","url_abs":"https://arxiv.org/abs/2511.06269","url_pdf":"https://arxiv.org/pdf/2511.06269v1","authors":"[\"Yuhao Zhang\",\"Qinghong Guo\",\"Qixian Chen\",\"Liuwei Zhang\",\"Hongyan Cui\",\"Xiyi Chen\"]","published":"2025-11-09T08:07:07Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"q-bio.QM\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false,"code_links":[{"ID":607282,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2844302,"paper_url":"https://arxiv.org/abs/2511.06269","paper_title":"LLM$^3$-DTI: A Large Language Model and Multi-modal data co-powered framework for Drug-Target Interaction prediction","repo_url":"https://github.com/chaser-gua/LLM3DTI","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}