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

• URL: http://arxiv.org/abs/2605.30247v1
• Date: Thu, 28 May 2026 17:12:48 GMT
• Title: OOD-GraphLLM: Graph Large Language Model for Out-of-Distribution Generalized Drug Synergy Prediction
• Abstract summary: We study out-of-distribution generalized drug synergy prediction through a graph large language model for the first time.<n>We propose OOD-GraphLLM, a novel graphLLM framework which is able to accurately predict drug synergy under O.O.D. settings.
• Score: 26.75075998637936
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Towards Interpretable Drug-Drug Interaction Prediction: A Graph-Based Approach with Molecular and Network-Level Explanations [3.6099926707292793]
  Drug-drug interactions (DDIs) represent a critical challenge in pharmacology, often leading to adverse drug reactions with significant implications for patient safety and healthcare outcomes.<n>We propose MolecBioNet, a novel graph-based framework that integrates molecular and biomedical knowledge for robust and interpretable DDI prediction.
  arXiv  Detail & Related papers  (2025-07-12T07:43:19Z)
• Addressing Model Overcomplexity in Drug-Drug Interaction Prediction With Molecular Fingerprints [0.0]
  Accurately predicting drug-drug interactions (DDIs) is crucial for pharmaceutical research and clinical safety.<n>Recent deep learning models often suffer from high computational costs and limited generalization across datasets.<n>In this study, we investigate a simpler yet effective approach using molecular representations such as Morgan fingerprints (S), graph-based embeddings from graph convolutional networks (GCNs), and transformer-derived embeddings from MoLFormer integrated into a straightforward neural network.
  arXiv  Detail & Related papers  (2025-03-30T18:27:01Z)
• Multimodal Contrastive Representation Learning in Augmented Biomedical Knowledge Graphs [2.006175707670159]
  PrimeKG++ is an enriched knowledge graph incorporating multimodal data.<n>Our approach demonstrates strong generalizability, enabling accurate link predictions even for unseen nodes.
  arXiv  Detail & Related papers  (2025-01-03T05:29:12Z)
• FARM: Functional Group-Aware Representations for Small Molecules [55.281754551202326]
  We introduce Functional Group-Aware Representations for Small Molecules (FARM)<n>FARM is a novel model designed to bridge the gap between SMILES, natural language, and molecular graphs.<n>We evaluate FARM on the MoleculeNet dataset, where it achieves state-of-the-art performance on 11 out of 13 tasks.
  arXiv  Detail & Related papers  (2024-10-02T23:04:58Z)
• Learning to Denoise Biomedical Knowledge Graph for Robust Molecular Interaction Prediction [50.7901190642594]
  We propose BioKDN (Biomedical Knowledge Graph Denoising Network) for robust molecular interaction prediction. BioKDN refines the reliable structure of local subgraphs by denoising noisy links in a learnable manner. It maintains consistent and robust semantics by smoothing relations around the target interaction.
  arXiv  Detail & Related papers  (2023-12-09T07:08:00Z)
• GraphCL-DTA: a graph contrastive learning with molecular semantics for drug-target binding affinity prediction [2.523552067304274]
  GraphCL-DTA is a graph contrastive learning framework for molecular graphs to learn drug representations. Next, we design a new loss function that can be directly used to adjust the uniformity of drug and target representations. The effectiveness of the above innovative elements is verified on two real datasets.
  arXiv  Detail & Related papers  (2023-07-18T06:01:37Z)
• Bi-level Contrastive Learning for Knowledge-Enhanced Molecule Representations [68.32093648671496]
  We introduce GODE, which accounts for the dual-level structure inherent in molecules.<n> Molecules possess an intrinsic graph structure and simultaneously function as nodes within a broader molecular knowledge graph.<n>By pre-training two GNNs on different graph structures, GODE effectively fuses molecular structures with their corresponding knowledge graph substructures.
  arXiv  Detail & Related papers  (2023-06-02T15:49:45Z)
• Hierarchical Graph Representation Learning for the Prediction of Drug-Target Binding Affinity [7.023929372010717]
  We propose a novel hierarchical graph representation learning model for the drug-target binding affinity prediction, namely HGRL-DTA. In this paper, we adopt a message broadcasting mechanism to integrate the hierarchical representations learned from the global-level affinity graph and the local-level molecular graph. Besides, we design a similarity-based embedding map to solve the cold start problem of inferring representations for unseen drugs and targets.
  arXiv  Detail & Related papers  (2022-03-22T04:50:16Z)
• Scientific Language Models for Biomedical Knowledge Base Completion: An Empirical Study [62.376800537374024]
  We study scientific LMs for KG completion, exploring whether we can tap into their latent knowledge to enhance biomedical link prediction. We integrate the LM-based models with KG embedding models, using a router method that learns to assign each input example to either type of model and provides a substantial boost in performance.
  arXiv  Detail & Related papers  (2021-06-17T17:55:33Z)
• Distance-aware Molecule Graph Attention Network for Drug-Target Binding Affinity Prediction [54.93890176891602]
  We propose a diStance-aware Molecule graph Attention Network (S-MAN) tailored to drug-target binding affinity prediction. As a dedicated solution, we first propose a position encoding mechanism to integrate the topological structure and spatial position information into the constructed pocket-ligand graph. We also propose a novel edge-node hierarchical attentive aggregation structure which has edge-level aggregation and node-level aggregation.
  arXiv  Detail & Related papers  (2020-12-17T17:44:01Z)
• Self-Supervised Graph Transformer on Large-Scale Molecular Data [73.3448373618865]
  We propose a novel framework, GROVER, for molecular representation learning. GROVER can learn rich structural and semantic information of molecules from enormous unlabelled molecular data. We pre-train GROVER with 100 million parameters on 10 million unlabelled molecules -- the biggest GNN and the largest training dataset in molecular representation learning.
  arXiv  Detail & Related papers  (2020-06-18T08:37:04Z)
• MolTrans: Molecular Interaction Transformer for Drug Target Interaction Prediction [68.5766865583049]
  Drug target interaction (DTI) prediction is a foundational task for in silico drug discovery. Recent years have witnessed promising progress for deep learning in DTI predictions. We propose a Molecular Interaction Transformer (TransMol) to address these limitations.
  arXiv  Detail & Related papers  (2020-04-23T18:56:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.