LKE-DTA: predicting drug-target binding affinity with large language model representations and knowledge graph embeddings.

Accurate prediction of drug-target binding affinity (DTA) is pivotal for drug discovery, yet current computational methods struggle to integrate heterogeneous biomedical knowledge and capture complex molecular interactions. We present LKE-DTA, a novel deep learning framework that synergistically integrates large language models (LLMs) with knowledge graphs (KGs) to create comprehensive multi-dimensional representations for drugs and proteins. Besides, we propose a Dual Multi-Head Attention mechanism that dynamically fuses heterogeneous embeddings and captures complex dependencies, thereby significantly enhancing predictive accuracy. On benchmark datasets, comprehensive evaluations under fivefold cross-validation demonstrate that LKE-DTA consistently outperforms state-of-the-art methods. On Davis, it reduces MSE and MAE by 14.7% and 8.2%, increases CI and r by 0.9% and 3.4%. On KIBA, it achieves reductions of 4.6% in MSE and 5.3% in MAE, with improvements of 0.8% in CI and 1.5% in r, while maintaining robust convergence. In cold-start evaluation, LKE-DTA shows strong generalization: in the Cold Drug setting, CI and r improve by 2.4% and 9.6%; in the Cold Target setting, MSE, MAE, CI, and r improve by 10.2%, 12.2%, 6.6%, and 9.0%. On an independent test set, it achieves the lowest MSE and MAE and the highest CI and r, surpassing the best baseline by 9.5%, 13.0%, 6.6% and 9.6%, respectively. This work demonstrates the significant potential of combining LLMs with KGs to address biomedical challenges, opening new avenues for drug design and precision medicine research.