Cosynllm: predicting drug combination synergy with LLM-generated descriptions

Drug combination therapy is a well-established strategy for treating complex diseases. However, the vast combinatorial space renders exhaustive experimental screening impractical and costly. Recent studies have shown that deep learning techniques can effectively prioritize synergistic drug combinations by leveraging their powerful nonlinear modeling and automatic feature extraction capabilities. Meanwhile, Large Language Models (LLMs) offer great promise in drug discovery. In this paper, we propose CoSynLLM, an LLM-assisted predictive framework for predicting drug combination synergy. We fully leverage the latent knowledge embedded in LLMs to generate semantic-level chemical information, complemented by drug fingerprints to incorporate explicit structural details, while cell line gene expression profiles represent the cellular context. To effectively merge drug and cell line representations, a hierarchical feature fusion strategy is employed to progressively integrate features through multiple stages for predicting drug combination synergy. Extensive experiments on two benchmark datasets, NCI-ALMANAC and O'Neil, demonstrate that CoSynLLM achieves competitive performance, highlighting its effectiveness in predicting drug combination synergy. In summary, CoSynLLM effectively identifies synergistic drug combinations, offering a robust and practical computational framework for predicting drug combination synergy.