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 $\text{L}$arge $\text{L}$anguage $\text{M}$odel and $\text{M}$ulti-$\text{M}$odel data co-powered $\text{D}$rug $\text{T}$arget $\text{I}$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.