OOD-GraphLLM: Graph Large Language Model for Out-of-Distribution Generalized Drug Synergy Prediction

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. Concretely, we first propose a target-adaptive disentangled molecular graph encoding model to distinguish target-relevant and target-irrelevant molecular representations for both seen and unseen drugs, then introduce a pairwise attentive graph architecture search algorithm that dynamically finds the best neural architectures to calculate molecular representations for different and new drug pairs, followed by our design of multi-level contextualized cellular feature alignment mechanism to incorporate cell line context information at both structural and semantic levels. 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. Extensive experiments under several O.O.D. settings demonstrate that the proposed \model consistently outperforms state-of-the-art approaches on various DSP tasks. 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.