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These include ineffectiveness against schistosome larvae, reduced efficacy in young children, and emerging drug resistance. Discovering new and active anti\u2010schistosome small molecules is therefore critical, but this process presents the challenge of low accuracy in computer\u2010aided methods. To address this issue, we proposed GNN\u2010DDAS, a novel deep learning framework based on graph neural networks (GNN), designed for drug discovery to identify active anti\u2010schistosome (DDAS)\u00a0small molecules. Initially, a multi\u2010layer perceptron was used to derive sequence features from various representations of small molecule SMILES. Next, GNN was employed to extract structural features from molecular graphs. Finally, the extracted sequence and structural features were then concatenated and fed into a fully connected network to predict active anti\u2010schistosome small molecules. Experimental results showed that GNN\u2010DDAS exhibited superior performance compared to the benchmark methods on both benchmark and real\u2010world application datasets. Additionally, the use of GNNExplainer model allowed us to analyze the key substructure features of small molecules, providing insight into the effectiveness of GNN\u2010DDAS. Overall, GNN\u2010DDAS provided a promising solution for discovering new and active anti\u2010schistosome small molecules.<\/jats:p>","DOI":"10.1002\/jcc.27490","type":"journal-article","created":{"date-parts":[[2024,8,27]],"date-time":"2024-08-27T09:34:50Z","timestamp":1724751290000},"page":"2825-2834","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["<scp>GNN<\/scp>\u2010<scp>DDAS<\/scp>: Drug discovery for identifying anti\u2010schistosome small molecules based on graph neural network"],"prefix":"10.1002","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1014-2558","authenticated-orcid":false,"given":"Xin","family":"Zeng","sequence":"first","affiliation":[{"name":"College of Mathematics and Computer Science Dali University  Dali China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng\u2010Kun","family":"Feng","sequence":"additional","affiliation":[{"name":"College of Mathematics and Computer Science Dali University  Dali China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shu\u2010Juan","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Endemic Diseases Yunnan Institute of Endemic Diseases Control and Prevention  Dali China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuang\u2010Qing","family":"Lv","sequence":"additional","affiliation":[{"name":"Institute of Surveying and Information Engineering West Yunnan University of Applied Science  Dali China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meng\u2010Liang","family":"Wen","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Conservation and Utilization of Bio\u2010Resources in Yunnan Yunnan University  Kunming China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Li","sequence":"additional","affiliation":[{"name":"College of Mathematics and Computer Science Dali University  Dali China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,8,27]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.pt.2016.08.002"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/cmdc.201800572"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/0163-7258(95)00026-7"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.4155\/fmc.15.27"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00436-012-3151-z"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0087594"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1021\/acsmedchemlett.8b00257"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jmedchem.3c00128"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbaa205"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbz152"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.2174\/1381612829666230412084137"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.drudis.2020.12.003"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41573-019-0024-5"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12859-022-04560-x"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCBB.2021.3077905"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105214"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2023.11.018"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106145"},{"key":"e_1_2_8_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.107512"},{"key":"e_1_2_8_21_1","doi-asserted-by":"publisher","DOI":"10.2174\/1568026614666140929124445"},{"key":"e_1_2_8_22_1","doi-asserted-by":"publisher","DOI":"10.1080\/17460441.2023.2174522"},{"key":"e_1_2_8_23_1","volume-title":"Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","author":"Liao Z.","year":"2019"},{"key":"e_1_2_8_24_1","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","author":"He K.","year":"2016"},{"key":"e_1_2_8_25_1","unstructured":"W.Zaremba I.Sutskever O.Vinyals Recurrent Neural Network Regularization. arXiv.2015."},{"key":"e_1_2_8_26_1","author":"Xu K.","year":"2019","journal-title":"Int. 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