A graph neural network approach for predicting drug susceptibility in the human microbiome

被引：3
作者：
Maryam [1 ]
Rehman M.U. [2 ]
Hussain I. [2 ]
Tayara H. [3 ]
Chong K.T. [1 ,4 ]
机构：
[1] Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju
[2] Khalifa University Center for Autonomous Robotic Systems (KUCARS), Khalifa University
[3] School of International Engineering and Science, Jeonbuk National University, Jeonju
[4] Advances Electronics and Information Research Centre, Jeonbuk National University, Jeonju
来源：
Computers in Biology and Medicine | 2024年 / 179卷
基金：
新加坡国家研究基金会;
关键词：
Bioinformatics; Graph neural network; Microbiome; Molecular docking;
D O I：
10.1016/j.compbiomed.2024.108729
中图分类号：
学科分类号：
摘要：
Recent studies have illuminated the critical role of the human microbiome in maintaining health and influencing the pharmacological responses of drugs. Clinical trials, encompassing approximately 150 drugs, have unveiled interactions with the gastrointestinal microbiome, resulting in the conversion of these drugs into inactive metabolites. It is imperative to explore the field of pharmacomicrobiomics during the early stages of drug discovery, prior to clinical trials. To achieve this, the utilization of machine learning and deep learning models is highly desirable. In this study, we have proposed graph-based neural network models, namely GCN, GAT, and GINCOV models, utilizing the SMILES dataset of drug microbiome. Our primary objective was to classify the susceptibility of drugs to depletion by gut microbiota. Our results indicate that the GINCOV surpassed the other models, achieving impressive performance metrics, with an accuracy of 93% on the test dataset. This proposed Graph Neural Network (GNN) model offers a rapid and efficient method for screening drugs susceptible to gut microbiota depletion and also encourages the improvement of patient-specific dosage responses and formulations. © 2024 Elsevier Ltd
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