Work

Cosynllm: predicting drug combination synergy with LLM-generated descriptions

Year: 2026

Type: article

Abstract: 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. This paper presents an LLM-assisted predictive framework for predicting drug combination synergy, which incorporates a hierarchical feature fusion strategy. LLMs are employed to generate chemical information for rich feature representations. The proposed hierarchical feature fusion strategy progressively integrates features from drugs and cell lines through multiple stages, enabling effective capture of critical interaction patterns.

Source: Journal of Cheminformatics

DOI: 10.1186/s13321-026-01158-w

Authors: Suwan Mao, Wenjie Tang, Li Li, Mang Jing, Yun Liu, Junjie Wang

Institutions: Nanjing Medical University, Jiangsu Province Hospital, Southeast University

Language: English

Topic: Computational Drug Discovery Methods

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,390