{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T14:04:39Z","timestamp":1773151479052,"version":"3.50.1"},"reference-count":35,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T00:00:00Z","timestamp":1718841600000},"content-version":"vor","delay-in-days":19,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32170090"],"award-info":[{"award-number":["32170090"]}],"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":["31971206"],"award-info":[{"award-number":["31971206"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,6,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Predicting protein\u2013ligand binding affinity is crucial in new drug discovery and development. However, most existing models rely on acquiring 3D structures of elusive proteins. Combining amino acid sequences with ligand sequences and better highlighting active sites are also significant challenges.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We propose an innovative neural network model called DEAttentionDTA, based on dynamic word embeddings and a self-attention mechanism, for predicting protein\u2013ligand binding affinity. DEAttentionDTA takes the 1D sequence information of proteins as input, including the global sequence features of amino acids, local features of the active pocket site, and linear representation information of the ligand molecule in the SMILE format. These three linear sequences are fed into a dynamic word-embedding layer based on a 1D convolutional neural network for embedding encoding and are correlated through a self-attention mechanism. The output affinity prediction values are generated using a linear layer. We compared DEAttentionDTA with various mainstream tools and achieved significantly superior results on the same dataset. We then assessed the performance of this model in the p38 protein family.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The resource codes are available at https:\/\/github.com\/whatamazing1\/DEAttentionDTA.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae319","type":"journal-article","created":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T00:45:57Z","timestamp":1718844357000},"source":"Crossref","is-referenced-by-count":15,"title":["DEAttentionDTA: protein\u2013ligand binding affinity prediction based on dynamic embedding and self-attention"],"prefix":"10.1093","volume":"40","author":[{"given":"Xiying","family":"Chen","sequence":"first","affiliation":[{"name":"Key Lab of Molecular Biophysics of Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology , Wuhan 430074, China"}]},{"given":"Jinsha","family":"Huang","sequence":"additional","affiliation":[{"name":"Key Lab of Molecular Biophysics of Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology , Wuhan 430074, China"}]},{"given":"Tianqiao","family":"Shen","sequence":"additional","affiliation":[{"name":"Key Lab of Molecular Biophysics of Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology , Wuhan 430074, China"}]},{"given":"Houjin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Lab of Molecular Biophysics of Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology , Wuhan 430074, China"}]},{"given":"Li","family":"Xu","sequence":"additional","affiliation":[{"name":"Key Lab of Molecular Biophysics of Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology , Wuhan 430074, China"}]},{"given":"Min","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Lab of Molecular Biophysics of Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology , Wuhan 430074, China"}]},{"given":"Xiaoman","family":"Xie","sequence":"additional","affiliation":[{"name":"Key Lab of Molecular Biophysics of Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology , Wuhan 430074, China"}]},{"given":"Yunjun","family":"Yan","sequence":"additional","affiliation":[{"name":"Key Lab of Molecular Biophysics of Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology , Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2543-203X","authenticated-orcid":false,"given":"Jinyong","family":"Yan","sequence":"additional","affiliation":[{"name":"Key Lab of Molecular Biophysics of Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology , Wuhan 430074, China"}]}],"member":"286","published-online":{"date-parts":[[2024,6,19]]},"reference":[{"key":"2024062208565278100_btae319-B1","doi-asserted-by":"crossref","first-page":"4633","DOI":"10.1093\/bioinformatics\/btaa544","article-title":"DeepCDA: deep cross-domain compound-protein affinity prediction through LSTM and convolutional neural networks","volume":"36","author":"Abbasi","year":"2020","journal-title":"Bioinformatics"},{"key":"2024062208565278100_btae319-B2","doi-asserted-by":"crossref","first-page":"D464","DOI":"10.1093\/nar\/gky1004","article-title":"RCSB Protein Data Bank: biological macromolecular structures enabling research and education in fundamental biology, biomedicine, biotechnology and energy","volume":"47","author":"Burley","year":"2019","journal-title":"Nucleic Acids 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