用图LLM解决新药组合预测中的分布外问题，提升泛化能力。
OOD-GraphLLM: Graph Large Language Model for Out-of-Distribution Generalized Drug Synergy Prediction
- 构建图语言模型联合学习分子结构与生物语义表示。
- 在分布外设置下实现跨细胞背景的药物协同预测，准确率显著提升。
- 适合药物研发、AI制药领域研究者使用，尤其关注泛化能力的场景。
药物协同预测（DSP）旨在识别不同细胞背景下有效药物组合。然而，新化合物不断出现导致分子骨架和尺寸变化，使药物协同数据在拓扑结构上产生分布外（O.O.D.）偏移。现有方法依赖于分布内假设，无法处理此类偏移。本文首次提出基于图大语言模型的分布外广义药物协同预测方法。面临三大挑战：如何区分与细胞靶点相关/无关的分子表征；如何找到准确计算分子表征的最优图神经网络架构；如何在大模型中联合利用分子结构与语义信息。为此，我们提出OOD-GraphLLM框架，通过统一优化分子图表示与生物医学语义表示，实现分布外环境下的精准预测。此外，我们微调了DrugSyn-LLM生物医学大模型，并采用检索增强的生物医学指令微调策略，对齐分子拓扑与语义信息，支持基于语言推理的分布外广义预测。源代码（https://github.com/EkkoXiao/Bio-GraphLLM）与发布模型（https://mn.cs.tsinghua.edu.cn/bio-graphllm/）均已公开，支持下载与在线交互。
原文摘要 · Abstract (English)
Drug synergy prediction (DSP) aims to identify efficacious drug combinations under various cellular contexts with different targets. However, the continual emergence of novel compounds results in variations in molecular scaffolds and sizes, causing drug synergy data to exhibit out-of-distribution (O.O.D.) shifts with respect to topological structure. Existing works rely on in-distribution (I.D.) assumption, failing to handle the O.O.D. shifts. To solve this problem, we study out-of-distribution generalized drug synergy prediction through a graph large language model for the first time. Nevertheless, O.O.D. generalized DSP is highly non-trivial, posing several challenges: i) how to discover structurally relevant and irrelevant molecular representations with respect to cell targets; ii) how to find the optimal graph neural architectures that accurately calculate molecular representations; and iii) how to jointly leverage molecular structural and semantic information in LLMs. To address these challenges, we propose OOD-GraphLLM, a novel graphLLM framework which is able to accurately predict drug synergy under O.O.D. settings via jointly optimizing molecular graph representation and biomedical semantic language representations in a unified manner. Furthermore, we finetune DrugSyn-LLM, a biomedical LLM, and employ a retrieval-augmented biomedical instruction tuning strategy to align molecular topological information and molecular semantic information with language-based reasoning for O.O.D. generalized DSP. Both the source code (https://github.com/EkkoXiao/Bio-GraphLLM) and released model (https://mn.cs.tsinghua.edu.cn/bio-graphllm/) are publicly available, where users are allowed to download model resources and interactively use the system through a web interface.
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