{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T15:07:50Z","timestamp":1783436870405,"version":"3.54.6"},"reference-count":26,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2023,12,23]],"date-time":"2023-12-23T00:00:00Z","timestamp":1703289600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82003654"],"award-info":[{"award-number":["82003654"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82341093"],"award-info":[{"award-number":["82341093"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFC3400501"],"award-info":[{"award-number":["2022YFC3400501"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFC3400500"],"award-info":[{"award-number":["2022YFC3400500"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai Science and Technology Development Funds","award":["20QA1406400"],"award-info":[{"award-number":["20QA1406400"]}]},{"name":"Shanghai Science and Technology Development Funds","award":["22ZR1441400"],"award-info":[{"award-number":["22ZR1441400"]}]},{"name":"Lingang Laboratory","award":["LG202102-01-03"],"award-info":[{"award-number":["LG202102-01-03"]}]},{"DOI":"10.13039\/501100012600","name":"ShanghaiTech University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012600","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai Frontiers Science Center for Biomacromolecules and Precision Medicine at ShanghaiTech University"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,1,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>The prediction of binding affinity between drug and target is crucial in drug discovery. However, the accuracy of current methods still needs to be improved. On the other hand, most deep learning methods focus only on the prediction of non-covalent (non-bonded) binding molecular systems, but neglect the cases of covalent binding, which has gained increasing attention in the field of drug development.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this work, a new attention-based model, A Transformer Encoder and Fingerprint combined Prediction method for Drug\u2013Target Affinity (TEFDTA) is proposed to predict the binding affinity for bonded and non-bonded drug\u2013target interactions. To deal with such complicated problems, we used different representations for protein and drug molecules, respectively. In detail, an initial framework was built by training our model using the datasets of non-bonded protein\u2013ligand interactions. For the widely used dataset Davis, an additional contribution of this study is that we provide a manually corrected Davis database. The model was subsequently fine-tuned on a smaller dataset of covalent interactions from the CovalentInDB database to optimize performance. The results demonstrate a significant improvement over existing approaches, with an average improvement of 7.6% in predicting non-covalent binding affinity and a remarkable average improvement of 62.9% in predicting covalent binding affinity compared to using BindingDB data alone. At the end, the potential ability of our model to identify activity cliffs was investigated through a case study. The prediction results indicate that our model is sensitive to discriminate the difference of binding affinities arising from small variances in the structures of compounds.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The codes and datasets of TEFDTA are available at https:\/\/github.com\/lizongquan01\/TEFDTA.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btad778","type":"journal-article","created":{"date-parts":[[2023,12,23]],"date-time":"2023-12-23T20:42:05Z","timestamp":1703364125000},"source":"Crossref","is-referenced-by-count":58,"title":["TEFDTA: a transformer encoder and fingerprint representation combined prediction method for bonded and non-bonded drug\u2013target affinities"],"prefix":"10.1093","volume":"40","author":[{"given":"Zongquan","family":"Li","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, ShanghaiTech University , Shanghai, 201210, China"},{"name":"Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University , Shanghai, 201210, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengxuan","family":"Ren","sequence":"additional","affiliation":[{"name":"Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University , Shanghai, 201210, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-0040-276X","authenticated-orcid":false,"given":"Hao","family":"Yang","sequence":"additional","affiliation":[{"name":"Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University , Shanghai, 201210, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6774-9786","authenticated-orcid":false,"given":"Jie","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, ShanghaiTech University , Shanghai, 201210, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1468-5568","authenticated-orcid":false,"given":"Fang","family":"Bai","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, ShanghaiTech University , Shanghai, 201210, China"},{"name":"Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University , Shanghai, 201210, China"},{"name":"Shanghai Clinical Research and Trial Center , Shanghai, 201210, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2023,12,23]]},"reference":[{"key":"2024080600403081900_btad778-B1","doi-asserted-by":"crossref","first-page":"4633","DOI":"10.1093\/bioinformatics\/btaa544","article-title":"DeepCDA: deep cross-domain compound\u2013protein affinity prediction through LSTM and convolutional neural networks","volume":"36","author":"Abbasi","year":"2020","journal-title":"Bioinformatics"},{"key":"2024080600403081900_btad778-B2","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1007\/978-1-4939-9752-7_10","article-title":"Molegro virtual docker for docking","volume":"2053","author":"Bitencourt-Ferreira","year":"2019","journal-title":"Methods Mol Biol"},{"key":"2024080600403081900_btad778-B3","doi-asserted-by":"crossref","first-page":"4217","DOI":"10.1038\/s41467-023-39856-w","article-title":"Sequence-based drug design as a concept in computational drug design","volume":"14","author":"Chen","year":"2023","journal-title":"Nat Commun"},{"key":"2024080600403081900_btad778-B4","doi-asserted-by":"crossref","first-page":"4406","DOI":"10.1093\/bioinformatics\/btaa524","article-title":"TransformerCPI: improving compound\u2013protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments","volume":"36","author":"Chen","year":"2020","journal-title":"Bioinformatics"},{"key":"2024080600403081900_btad778-B5","doi-asserted-by":"crossref","first-page":"1046","DOI":"10.1038\/nbt.1990","article-title":"Comprehensive analysis of kinase inhibitor selectivity","volume":"29","author":"Davis","year":"2011","journal-title":"Nat Biotechnol"},{"key":"2024080600403081900_btad778-B6","doi-asserted-by":"crossref","first-page":"D1122","DOI":"10.1093\/nar\/gkaa876","article-title":"CovalentInDB: a comprehensive database facilitating the discovery of covalent inhibitors","volume":"49","author":"Du","year":"2021","journal-title":"Nucleic Acids Res"},{"key":"2024080600403081900_btad778-B7","doi-asserted-by":"crossref","first-page":"1739","DOI":"10.1021\/jm0306430","article-title":"Glide: a new approach for rapid, accurate docking and scoring. 1. 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