{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T12:23:05Z","timestamp":1784118185976,"version":"3.55.0"},"reference-count":52,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2023,1,23]],"date-time":"2023-01-23T00:00:00Z","timestamp":1674432000000},"content-version":"vor","delay-in-days":0,"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":["62002251"],"award-info":[{"award-number":["62002251"]}],"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":["62272335"],"award-info":[{"award-number":["62272335"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Jiangsu Province Youth Fund","award":["BK20200856"],"award-info":[{"award-number":["BK20200856"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,2,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Accurate and rapid prediction of protein\u2013ligand binding affinity is a great challenge currently encountered in drug discovery. Recent advances have manifested a promising alternative in applying deep learning-based computational approaches for accurately quantifying binding affinity. The structure complementarity between protein-binding pocket and ligand has a great effect on the binding strength between a protein and a ligand, but most of existing deep learning approaches usually extracted the features of pocket and ligand by these two detached modules.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this work, a new deep learning approach based on the cross-attention mechanism named CAPLA was developed for improved prediction of protein\u2013ligand binding affinity by learning features from sequence-level information of both protein and ligand. Specifically, CAPLA employs the cross-attention mechanism to capture the mutual effect of protein-binding pocket and ligand. We evaluated the performance of our proposed CAPLA on comprehensive benchmarking experiments on binding affinity prediction, demonstrating the superior performance of CAPLA over state-of-the-art baseline approaches. Moreover, we provided the interpretability for CAPLA to uncover critical functional residues that contribute most to the binding affinity through the analysis of the attention scores generated by the cross-attention mechanism. Consequently, these results indicate that CAPLA is an effective approach for binding affinity prediction and may contribute to useful help for further consequent applications.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The source code of the method along with trained models is freely available at https:\/\/github.com\/lennylv\/CAPLA.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btad049","type":"journal-article","created":{"date-parts":[[2023,1,23]],"date-time":"2023-01-23T14:52:15Z","timestamp":1674485535000},"source":"Crossref","is-referenced-by-count":88,"title":["CAPLA: improved prediction of protein\u2013ligand binding affinity by a deep learning approach based on a cross-attention mechanism"],"prefix":"10.1093","volume":"39","author":[{"given":"Zhi","family":"Jin","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Soochow University , Suzhou 215006, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8137-2436","authenticated-orcid":false,"given":"Tingfang","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Soochow University , Suzhou 215006, China"},{"name":"Province Key Lab for Information Processing Technologies, Soochow University , Suzhou 215006, China"},{"name":"Collaborative Innovation Center of Novel Software Technology and Industrialization , Nanjing 210000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Taoning","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Soochow University , Suzhou 215006, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deng","family":"Pan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Soochow University , Suzhou 215006, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuejiao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Soochow University , Suzhou 215006, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingxin","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Soochow University , Suzhou 215006, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8103-555X","authenticated-orcid":false,"given":"Lijun","family":"Quan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Soochow University , Suzhou 215006, China"},{"name":"Province Key Lab for Information Processing Technologies, Soochow University , Suzhou 215006, China"},{"name":"Collaborative Innovation Center of Novel Software Technology and Industrialization , Nanjing 210000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiang","family":"Lyu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Soochow University , Suzhou 215006, China"},{"name":"Province Key Lab for Information Processing Technologies, Soochow University , Suzhou 215006, China"},{"name":"Collaborative Innovation Center of Novel Software Technology and Industrialization , Nanjing 210000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2023,1,23]]},"reference":[{"key":"2023020606330510400_btad049-B1","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.1093\/bioinformatics\/btq112","article-title":"A machine learning approach to predicting protein\u2013ligand binding affinity with applications to molecular docking","volume":"26","author":"Ballester","year":"2010","journal-title":"Bioinformatics"},{"key":"2023020606330510400_btad049-B2","doi-asserted-by":"crossref","first-page":"3647","DOI":"10.1080\/01483918708077820","article-title":"TLC resolution of amino acids in a new solvent and effect of alkaline earth metals","volume":"10","author":"Bhushan","year":"1987","journal-title":"J. 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