CS-DTA: a language model-driven framework for robust drug-target affinity prediction under strict cold-start scenarios
Shanwen Sun
IntroductionAccurate prediction of drug-target affinity is important for computational drug discovery, yet many deep learning models show limited robustness when applied to unseen compounds or previously uncharacterized proteins.MethodsWe developed CS-DTA, a modular DTA prediction framework that integrates large language models (LLMs) for compound and protein representation learning with a cross-modal interaction module. By leveraging the strong transferability of LLM-based encoders, the model c