Emerging Drug Interaction Prediction Enabled by Flow-based Graph Neural Network with Biomedical Network

• URL: http://arxiv.org/abs/2311.09261v1
• Date: Wed, 15 Nov 2023 06:34:00 GMT
• Title: Emerging Drug Interaction Prediction Enabled by Flow-based Graph Neural Network with Biomedical Network
• Authors: Yongqi Zhang, Quanming Yao, Ling Yue, Xian Wu, Ziheng Zhang, Zhenxi Lin, Yefeng Zheng
• Abstract summary: We propose EmerGNN, a graph neural network (GNN) that can effectively predict interactions for emerging drugs. EmerGNN learns pairwise representations of drugs by extracting the paths between drug pairs, propagating information from one drug to the other, and incorporating the relevant biomedical concepts on the paths. Overall, EmerGNN has higher accuracy than existing approaches in predicting interactions for emerging drugs and can identify the most relevant information on the biomedical network.
• Score: 69.16939798838159
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurately predicting drug-drug interactions (DDI) for emerging drugs, which offer possibilities for treating and alleviating diseases, with computational methods can improve patient care and contribute to efficient drug development. However, many existing computational methods require large amounts of known DDI information, which is scarce for emerging drugs. In this paper, we propose EmerGNN, a graph neural network (GNN) that can effectively predict interactions for emerging drugs by leveraging the rich information in biomedical networks. EmerGNN learns pairwise representations of drugs by extracting the paths between drug pairs, propagating information from one drug to the other, and incorporating the relevant biomedical concepts on the paths. The different edges on the biomedical network are weighted to indicate the relevance for the target DDI prediction. Overall, EmerGNN has higher accuracy than existing approaches in predicting interactions for emerging drugs and can identify the most relevant information on the biomedical network.

Related papers

• DrugAgent: Explainable Drug Repurposing Agent with Large Language Model-based Reasoning [10.528489471229946]
  We propose a multi-agent framework to enhance the drug repurposing process using state-of-the-art machine learning techniques and knowledge integration. Our framework comprises several specialized agents: an AI Agent trains robust drug-target interaction (DTI) models; a Knowledge Graph Agent utilizes the drug-gene interaction database (DGIdb) to systematically extract DTIs. By integrating outputs from these agents, our system effectively harnesses diverse data sources, including external databases, to propose viable repurposing candidates.
  arXiv  Detail & Related papers  (2024-08-23T21:24:59Z)
• A Cross-Field Fusion Strategy for Drug-Target Interaction Prediction [85.2792480737546]
  Existing methods fail to utilize global protein information during DTI prediction. Cross-field information fusion strategy is employed to acquire local and global protein information. Siamese drug-target interaction SiamDTI prediction method achieves higher accuracy levels than other state-of-the-art (SOTA) methods on novel drugs and targets.
  arXiv  Detail & Related papers  (2024-05-23T13:25:20Z)
• 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)
• DDoS: A Graph Neural Network based Drug Synergy Prediction Algorithm [0.521420263116111]
  We introduce a Graph Neural Network (textitGNN) based model for drug synergy prediction. In contrast to conventional models, our GNN-based approach learns task-specific drug representations directly from the graph structure of the drugs. Our work suggests that learning task-specific drug representations and leveraging a diverse dataset is a promising approach to advancing our understanding of drug-drug interaction and synergy.
  arXiv  Detail & Related papers  (2022-10-03T10:16:29Z)
• Multi-View Substructure Learning for Drug-Drug Interaction Prediction [69.34322811160912]
  We propose a novel multi- view drug substructure network for DDI prediction (MSN-DDI) MSN-DDI learns chemical substructures from both the representations of the single drug (intra-view) and the drug pair (inter-view) simultaneously and utilizes the substructures to update the drug representation iteratively. Comprehensive evaluations demonstrate that MSN-DDI has almost solved DDI prediction for existing drugs by achieving a relatively improved accuracy of 19.32% and an over 99% accuracy under the transductive setting.
  arXiv  Detail & Related papers  (2022-03-28T05:44:29Z)
• DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise Annotations [90.27736364704108]
  We present DrugOOD, a systematic OOD dataset curator and benchmark for AI-aided drug discovery. DrugOOD comes with an open-source Python package that fully automates benchmarking processes. We focus on one of the most crucial problems in AIDD: drug target binding affinity prediction.
  arXiv  Detail & Related papers  (2022-01-24T12:32:48Z)
• A Review of Biomedical Datasets Relating to Drug Discovery: A Knowledge Graph Perspective [4.746544835197422]
  We aim to help guide machine learning and knowledge graph practitioners who are interested in applying new techniques to the drug discovery field. We detail publicly available primary data sources containing information suitable for use in constructing various drug discovery focused knowledge graphs.
  arXiv  Detail & Related papers  (2021-02-19T17:49:38Z)
• Heterogeneous Graph based Deep Learning for Biomedical Network Link Prediction [7.628651624423363]
  We propose a Graph Pair based Link Prediction model (GPLP) for predicting biomedical network links. InP, 1-hop subgraphs extracted from known network interaction matrix is learnt to predict missing links. Our method demonstrates the potential applications in other biomedical networks.
  arXiv  Detail & Related papers  (2021-01-28T07:35:29Z)
• Predicting Biomedical Interactions with Higher-Order Graph Convolutional Networks [2.9488233765621295]
  We present a higher-order graph convolutional network (HOGCN) to aggregate information from the higher-order neighborhood for biomedical interaction prediction. Experiments on four interaction networks, including protein-protein, drug-drug, drug-target, and gene-disease interactions, show that HOGCN achieves more accurate and calibrated predictions.
  arXiv  Detail & Related papers  (2020-10-16T17:16:09Z)
• SumGNN: Multi-typed Drug Interaction Prediction via Efficient Knowledge Graph Summarization [64.56399911605286]
  We propose SumGNN: knowledge summarization graph neural network, which is enabled by a subgraph extraction module. SumGNN outperforms the best baseline by up to 5.54%, and the performance gain is particularly significant in low data relation types.
  arXiv  Detail & Related papers  (2020-10-04T00:14:57Z)

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.