DrugLM: A Unified Framework to Enhance Drug–Target Interaction Prediction by Incorporating Textual Embeddings via Language Models

Accurate prediction of drug–target interactions (DTIs) is a cornerstone of computational drug discovery, with the potential to reduce experimental costs and accelerate therapeutic development. Although deep learning methods such as graph neural networks and Transformers have achieved notable success in predicting DTIs, they often fail to fully exploit the rich semantic information contained in textual descriptions of drugs and targets. We developed DrugLM, a unified framework that integrates embeddings from large language models (LLMs) into DTI prediction architectures. Textual descriptions of drugs and targets were encoded using pre-trained LLMs, with optional selective upper-layer fine-tuning for task adaptation. The resulting embeddings were incorporated into six representative backbone models and evaluated on a curated DrugBank-derived dataset. Incorporating LLM-derived embeddings improved prediction performance across the evaluated architectures. Even without task-specific adaptation, pre-trained embeddings outperformed random or conventional initialization, while selective fine-tuning yielded additional gains in many settings. A simple multilayer perceptron using only LLM-derived embeddings also surpassed several established DTI methods, highlighting the predictive value of semantic representations. DrugLM provides an architecture-flexible strategy for incorporating semantic information from textual descriptions into the evaluated DTI prediction pipelines. These findings motivate further evaluation on additional datasets and protocols.