{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T21:38:52Z","timestamp":1784842732090,"version":"3.55.0"},"reference-count":37,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2023,1,27]],"date-time":"2023-01-27T00:00:00Z","timestamp":1674777600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012456","name":"National Social Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012456","id-type":"DOI","asserted-by":"publisher"}]}],"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>Recently, deep learning has become the mainstream methodology for drug\u2013target binding affinity prediction. However, two deficiencies of the existing methods restrict their practical applications. On the one hand, most existing methods ignore the individual information of sequence elements, resulting in poor sequence feature representations. On the other hand, without prior biological knowledge, the prediction of drug\u2013target binding regions based on attention weights of a deep neural network could be difficult to verify, which may bring adverse interference to biological researchers.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We propose a novel Multi-Functional and Robust Drug\u2013Target binding Affinity prediction (MFR-DTA) method to address the above issues. Specifically, we design a new biological sequence feature extraction block, namely BioMLP, that assists the model in extracting individual features of sequence elements. Then, we propose a new Elem-feature fusion block to refine the extracted features. After that, we construct a Mix-Decoder block that extracts drug\u2013target interaction information and predicts their binding regions simultaneously. Last, we evaluate MFR-DTA on two benchmarks consistently with the existing methods and propose a new dataset, sc-PDB, to better measure the accuracy of binding region prediction. We also visualize some samples to demonstrate the locations of their binding sites and the predicted multi-scale interaction regions. The proposed method achieves excellent performance on these datasets, demonstrating its merits and superiority over the state-of-the-art methods.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>https:\/\/github.com\/JU-HuaY\/MFR.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btad056","type":"journal-article","created":{"date-parts":[[2023,1,28]],"date-time":"2023-01-28T07:49:12Z","timestamp":1674892152000},"source":"Crossref","is-referenced-by-count":80,"title":["MFR-DTA: a multi-functional and robust model for predicting drug\u2013target binding affinity and region"],"prefix":"10.1093","volume":"39","author":[{"given":"Yang","family":"Hua","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence and Computer Science, Jiangnan University , Wuxi 214122, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5741-9318","authenticated-orcid":false,"given":"Xiaoning","family":"Song","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Computer Science, Jiangnan University , Wuxi 214122, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenhua","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Electronic Engineering, University of Surrey , Guildford GU2 7XH, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaojun","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Computer Science, Jiangnan University , Wuxi 214122, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2023,1,27]]},"reference":[{"key":"2023020606332190500_btad056-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":"2023020606332190500_btad056-B2","doi-asserted-by":"crossref","first-page":"W441","DOI":"10.1093\/nar\/gkp253","article-title":"IC 50-to-Ki: a web-based tool for converting IC50 to Ki values for inhibitors of enzyme activity and ligand binding","volume":"37","author":"Cer","year":"2009","journal-title":"Nucleic Acids Res"},{"key":"2023020606332190500_btad056-B3","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":"2023020606332190500_btad056-B4","doi-asserted-by":"crossref","first-page":"2208","DOI":"10.3390\/molecules23092208","article-title":"Machine learning for drug-target interaction prediction","volume":"23","author":"Chen","year":"2018","journal-title":"Molecules"},{"key":"2023020606332190500_btad056-B5","doi-asserted-by":"crossref","first-page":"2373","DOI":"10.1039\/c2mb25110h","article-title":"Prediction of chemical\u2013protein interactions: multitarget-QSAR versus computational chemogenomic methods","volume":"8","author":"Cheng","year":"2012","journal-title":"Mol. 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