# LLM3-DTI: A Large Language Model and Multi-modal data co-powered framework for Drug-Target Interaction prediction

arXiv: https://arxiv.org/abs/2511.06269 · HTML: https://mlsanity.com/paper/2511.06269/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 33 references that are in the map.
- First posted on arXiv 9 Nov 2025.

## Builds on (most cited first, 3 of 3)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [UrbanCLIP: Learning Text-enhanced Urban Region Profiling with Contrastive Language-Image Pretraining from the Web](https://mlsanity.com/paper/2310.18340/) (2023)
- [Drug-Target Interaction Prediction with Graph Attention networks](https://mlsanity.com/paper/2107.06099/) (2021)

## Built on by (most cited first, 1 of 1)

- [Scaling-Aware Adapter for Structure-Grounded LLM Reasoning](https://mlsanity.com/paper/2602.02780/) (2026)

## Abstract (arXiv)

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 Large Language Model and Multi-Model data co-powered Drug Target Interaction prediction framework, named LLM3-DTI. LLM3-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 LLM3-DTI can proficiently identify validated DTIs, surpassing the performance of the models employed for comparison across diverse scenarios. Consequently, LLM3-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.