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For accurate predictions, many classical scoring functions and machine learning-based methods have been developed. However, these techniques tend to have limitations, mainly resulting from a lack of sufficient energy terms to describe the complex interactions between proteins and ligands. Recent deep-learning techniques can potentially solve this problem. However, the search for more efficient and appropriate deep-learning architectures and methods to represent protein\u2013ligand complex is ongoing.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In this study, we proposed a deep-neural network model to improve the prediction accuracy of protein\u2013ligand complex binding affinity. The proposed model has two important features, descriptor embeddings with information on the local structures of a protein\u2013ligand complex and an attention mechanism to highlight important descriptors for binding affinity prediction. The proposed model performed better than existing binding affinity prediction models on most benchmark datasets.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      We confirmed that an attention mechanism can capture the binding sites in a protein\u2013ligand complex to improve prediction performance. Our code is available at\n                      <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/Blue1993\/BAPA\">https:\/\/github.com\/Blue1993\/BAPA<\/jats:ext-link>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12859-021-04466-0","type":"journal-article","created":{"date-parts":[[2021,11,8]],"date-time":"2021-11-08T09:02:54Z","timestamp":1636362174000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":83,"title":["Binding affinity prediction for protein\u2013ligand complex using deep attention mechanism based on intermolecular interactions"],"prefix":"10.1186","volume":"22","author":[{"given":"Sangmin","family":"Seo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonghwan","family":"Choi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sanghyun","family":"Park","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jaegyoon","family":"Ahn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,11,8]]},"reference":[{"issue":"4","key":"4466_CR1","doi-asserted-by":"publisher","first-page":"312","DOI":"10.2174\/138920307781369382","volume":"8","author":"RT Kroemer","year":"2007","unstructured":"Kroemer RT. 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