{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T16:36:47Z","timestamp":1778344607146,"version":"3.51.4"},"reference-count":68,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2022,9,17]],"date-time":"2022-09-17T00:00:00Z","timestamp":1663372800000},"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\/501100003453","name":"Natural Science Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2020A1515010548"],"award-info":[{"award-number":["2020A1515010548"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81973241"],"award-info":[{"award-number":["81973241"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,11,19]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Accurate prediction of molecular properties, such as physicochemical and bioactive properties, as well as ADME\/T (absorption, distribution, metabolism, excretion and toxicity) properties, remains a fundamental challenge for molecular design, especially for drug design and discovery. In this study, we advanced a novel deep learning architecture, termed FP-GNN (fingerprints and graph neural networks), which combined and simultaneously learned information from molecular graphs and fingerprints for molecular property prediction. To evaluate the FP-GNN model, we conducted experiments on 13 public datasets, an unbiased LIT-PCBA dataset and 14 phenotypic screening datasets for breast cell lines. Extensive evaluation results showed that compared to advanced deep learning and conventional machine learning algorithms, the FP-GNN algorithm achieved state-of-the-art performance on these datasets. In addition, we analyzed the influence of different molecular fingerprints, and the effects of molecular graphs and molecular fingerprints on the performance of the FP-GNN model. Analysis of the anti-noise ability and interpretation ability also indicated that FP-GNN was competitive in real-world situations. Collectively, FP-GNN algorithm can assist chemists, biologists and pharmacists in predicting and discovering better molecules with desired functions or properties.<\/jats:p>","DOI":"10.1093\/bib\/bbac408","type":"journal-article","created":{"date-parts":[[2022,9,20]],"date-time":"2022-09-20T09:10:39Z","timestamp":1663665039000},"source":"Crossref","is-referenced-by-count":212,"title":["FP-GNN: a versatile deep learning architecture for enhanced molecular property prediction"],"prefix":"10.1093","volume":"23","author":[{"given":"Hanxuan","family":"Cai","sequence":"first","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology , Guangzhou 510006, China"}]},{"given":"Huimin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology , Guangzhou 510006, China"}]},{"given":"Duancheng","family":"Zhao","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology , Guangzhou 510006, China"}]},{"given":"Jingxing","family":"Wu","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology , Guangzhou 510006, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5116-7749","authenticated-orcid":false,"given":"Ling","family":"Wang","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology , Guangzhou 510006, China"}]}],"member":"286","published-online":{"date-parts":[[2022,9,17]]},"reference":[{"key":"2022112111112587200_ref1","doi-asserted-by":"crossref","DOI":"10.3390\/molecules25061292","article-title":"QSPR\/QSAR: state-of-art, weirdness, the future","volume":"25","author":"Toropov","year":"2020","journal-title":"Molecules"},{"key":"2022112111112587200_ref2","doi-asserted-by":"crossref","first-page":"3525","DOI":"10.1039\/D0CS00098A","article-title":"QSAR without borders","volume":"49","author":"Muratov","year":"2020","journal-title":"Chem Soc Rev"},{"key":"2022112111112587200_ref3","doi-asserted-by":"crossref","first-page":"1638","DOI":"10.1021\/jm049228d","article-title":"A general method for exploiting QSAR models in lead optimization","volume":"48","author":"Lewis","year":"2005","journal-title":"J Med Chem"},{"key":"2022112111112587200_ref4","doi-asserted-by":"crossref","first-page":"4977","DOI":"10.1021\/jm4004285","article-title":"QSAR modeling: where have you been? 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