{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,19]],"date-time":"2026-07-19T18:15:10Z","timestamp":1784484910237,"version":"3.55.0"},"reference-count":67,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2022,8,26]],"date-time":"2022-08-26T00:00:00Z","timestamp":1661472000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"publisher","award":["2017YFC1104400"],"award-info":[{"award-number":["2017YFC1104400"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,9,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Natural products (NPs) and their derivatives are important resources for drug discovery. There are many in silico target prediction methods that have been reported, however, very few of them distinguish NPs from synthetic molecules. Considering the fact that NPs and synthetic molecules are very different in many characteristics, it is necessary to build specific target prediction models of NPs. Therefore, we collected the activity data of NPs and their derivatives from the public databases and constructed four datasets, including the NP dataset, the NPs and its first-class derivatives dataset, the NPs and all its derivatives and the ChEMBL26 compounds dataset. Conditions, including activity thresholds and input features, were explored to access the performance of eight machine learning methods of target prediction of NPs, including support vector machines (SVM), extreme gradient boosting, random forests, K-nearest neighbor, naive Bayes, feedforward neural networks (FNN), convolutional neural networks and recurrent neural networks. As a result, the NPs and all their derivatives datasets were selected to build the best NP-specific models. Furthermore, the consensus models, as well as the voting models, were additionally applied to improve the prediction performance. More evaluations were made on the external validation set and the results demonstrated that (1) the NP-specific model performed better on the target prediction of NPs than the traditional models training on the whole compounds of ChEMBL26. (2) The consensus model of FNN\u2009+\u2009SVM possessed the best overall performance, and the voting model can significantly improve recall and specificity.<\/jats:p>","DOI":"10.1093\/bib\/bbac359","type":"journal-article","created":{"date-parts":[[2022,8,25]],"date-time":"2022-08-25T20:27:53Z","timestamp":1661459273000},"source":"Crossref","is-referenced-by-count":13,"title":["Large-scale comparison of machine learning algorithms for target prediction of natural products"],"prefix":"10.1093","volume":"23","author":[{"given":"Lu","family":"Liang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Medicinal Chemical Biology, College of Pharmacy and Tianjin Key Laboratory of Molecular Drug Research, Nankai University , Haihe Education Park, 38 Tongyan Road, Tianjin 300353 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ye","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Medicinal Chemical Biology, College of Pharmacy and Tianjin Key Laboratory of Molecular Drug Research, Nankai University , Haihe Education Park, 38 Tongyan Road, Tianjin 300353 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Kang","sequence":"additional","affiliation":[{"name":"National Supercomputer Center in Tianjin , 10 Xinhuanxi Road, Tianjin Binhai New Area, Tianjin 300457 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ru","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Medicinal Chemical Biology, College of Pharmacy and Tianjin Key Laboratory of Molecular Drug Research, Nankai University , Haihe Education Park, 38 Tongyan Road, Tianjin 300353 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meng-Yu","family":"Sun","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Medicinal Chemical Biology, College of Pharmacy and Tianjin Key Laboratory of Molecular Drug Research, Nankai University , Haihe Education Park, 38 Tongyan Road, Tianjin 300353 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi","family":"Wu","sequence":"additional","affiliation":[{"name":"National Supercomputer Center in Tianjin , 10 Xinhuanxi Road, Tianjin Binhai New Area, Tianjin 300457 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiang-Fei","family":"Meng","sequence":"additional","affiliation":[{"name":"National Supercomputer Center in Tianjin , 10 Xinhuanxi Road, Tianjin Binhai New Area, Tianjin 300457 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6974-0072","authenticated-orcid":false,"given":"Jian-Ping","family":"Lin","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Medicinal Chemical Biology, College of Pharmacy and Tianjin Key Laboratory of Molecular Drug Research, Nankai University , Haihe Education Park, 38 Tongyan Road, Tianjin 300353 , 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