Building a unified model for drug synergy analysis powered by large language models

Tianyu Liu, Tinyi Chu, Xiao Luo and Hongyu Zhao ()
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Tianyu Liu: Yale University
Tinyi Chu: Yale University
Xiao Luo: University of California, Los Angeles
Hongyu Zhao: Yale University

Nature Communications, 2025, vol. 16, issue 1, 1-17

Abstract: Abstract Drug synergy prediction is a challenging and important task in the treatment of complex diseases including cancer. In this manuscript, we present a unified Model, known as BAITSAO, for tasks related to drug synergy prediction with a unified pipeline to handle different datasets. We construct the training datasets for BAITSAO based on the context-enriched embeddings from Large Language Models for the initial representation of drugs and cell lines. After demonstrating the relevance of these embeddings, we pre-train BAITSAO with a large-scale drug synergy database under a multi-task learning framework with rigorous selections of tasks. We demonstrate the superiority of the model architecture and the pre-trained strategies of BAITSAO over other methods through comprehensive benchmark analysis. Moreover, we investigate the sensitivity of BAITSAO and illustrate its promising functions including drug discoveries, drug combinations-gene interaction, and multi-drug synergy predictions.

Date: 2025
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DOI: 10.1038/s41467-025-59822-y

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