{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T15:35:37Z","timestamp":1759937737986,"version":"3.41.2"},"reference-count":33,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,4,7]],"date-time":"2022-04-07T00:00:00Z","timestamp":1649289600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Microbiol."],"abstract":"<jats:p>Human beings are now facing one of the largest public health crises in history with the outbreak of COVID-19. Traditional drug discovery could not keep peace with newly discovered infectious diseases. The prediction of drug-virus associations not only provides insights into the mechanism of drug\u2013virus interactions, but also guides the screening of potential antiviral drugs. We develop a deep learning algorithm based on the graph convolutional networks (MDGNN) to predict potential antiviral drugs. MDGNN is consisted of new node-level attention and feature-level attention mechanism and shows its effectiveness compared with other comparative algorithms. MDGNN integrates the global information of the graph in the process of information aggregation by introducing the attention at node and feature level to graph convolution. Comparative experiments show that MDGNN achieves state-of-the-art performance with an area under the curve (AUC) of 0.9726 and an area under the PR curve (AUPR) of 0.9112. In this case study, two drugs related to SARS-CoV-2 were successfully predicted and verified by the relevant literature. The data and code are open source and can be accessed from <jats:ext-link>https:\/\/github.com\/Pijiangsheng\/MDGNN<\/jats:ext-link>.<\/jats:p>","DOI":"10.3389\/fmicb.2022.819046","type":"journal-article","created":{"date-parts":[[2022,4,7]],"date-time":"2022-04-07T04:56:03Z","timestamp":1649307363000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["MDGNN: Microbial Drug Prediction Based on Heterogeneous Multi-Attention Graph Neural Network"],"prefix":"10.3389","volume":"13","author":[{"given":"Jiangsheng","family":"Pi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peishun","family":"Jiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junyi","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2022,4,7]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1016\/j.ijid.2020.02.018","article-title":"Discovery and development of safe-in-man broad-spectrum antiviral agents.","volume":"93","author":"Andersen","year":"2020","journal-title":"Int. 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