{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T10:06:44Z","timestamp":1784110004384,"version":"3.55.0"},"reference-count":44,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2023,12,8]],"date-time":"2023-12-08T00:00:00Z","timestamp":1701993600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Sciences Foundation of China","award":["62241602"],"award-info":[{"award-number":["62241602"]}]},{"name":"National Natural Sciences Foundation of China","award":["202101BA070001-227"],"award-info":[{"award-number":["202101BA070001-227"]}]},{"name":"National Natural Sciences Foundation of China","award":["APR202110"],"award-info":[{"award-number":["APR202110"]}]},{"name":"Yunnan Fundamental Research Projects","award":["62241602"],"award-info":[{"award-number":["62241602"]}]},{"name":"Yunnan Fundamental Research Projects","award":["202101BA070001-227"],"award-info":[{"award-number":["202101BA070001-227"]}]},{"name":"Yunnan Fundamental Research Projects","award":["APR202110"],"award-info":[{"award-number":["APR202110"]}]},{"name":"Yunnan Key Laboratory of Screening and Research on Anti-pathogenic Plant Resources from Western Yunnan","award":["62241602"],"award-info":[{"award-number":["62241602"]}]},{"name":"Yunnan Key Laboratory of Screening and Research on Anti-pathogenic Plant Resources from Western Yunnan","award":["202101BA070001-227"],"award-info":[{"award-number":["202101BA070001-227"]}]},{"name":"Yunnan Key Laboratory of Screening and Research on Anti-pathogenic Plant Resources from Western Yunnan","award":["APR202110"],"award-info":[{"award-number":["APR202110"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Molecules"],"abstract":"<jats:p>Drug\u2013target affinity (DTA) prediction is crucial for understanding molecular interactions and aiding drug discovery and development. While various computational methods have been proposed for DTA prediction, their predictive accuracy remains limited, failing to delve into the structural nuances of interactions. With increasingly accurate and accessible structure prediction of targets, we developed a novel deep learning model, named S2DTA, to accurately predict DTA by fusing sequence features of drug SMILES, targets, and pockets and their corresponding graph structural features using heterogeneous models based on graph and semantic networks. Experimental findings underscored that complex feature representations imparted negligible enhancements to the model\u2019s performance. However, the integration of heterogeneous models demonstrably bolstered predictive accuracy. In comparison to three state-of-the-art methodologies, such as DeepDTA, GraphDTA, and DeepDTAF, S2DTA\u2019s performance became more evident. It exhibited a 25.2% reduction in mean absolute error (MAE) and a 20.1% decrease in root mean square error (RMSE). Additionally, S2DTA showed some improvements in other crucial metrics, including Pearson Correlation Coefficient (PCC), Spearman, Concordance Index (CI), and R2, with these metrics experiencing increases of 19.6%, 17.5%, 8.1%, and 49.4%, respectively. Finally, we conducted an interpretability analysis on the effectiveness of S2DTA by bidirectional self-attention mechanism. The analysis results supported that S2DTA was an effective and accurate tool for predicting DTA.<\/jats:p>","DOI":"10.3390\/molecules28248005","type":"journal-article","created":{"date-parts":[[2023,12,8]],"date-time":"2023-12-08T03:03:33Z","timestamp":1702004613000},"page":"8005","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Fusing Sequence and Structural Knowledge by Heterogeneous Models to Accurately and Interpretively Predict Drug\u2013Target Affinity"],"prefix":"10.3390","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1014-2558","authenticated-orcid":false,"given":"Xin","family":"Zeng","sequence":"first","affiliation":[{"name":"College of Mathematics and Computer Science, Dali University, Dali 671003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4490-0575","authenticated-orcid":false,"given":"Kai-Yang","family":"Zhong","sequence":"additional","affiliation":[{"name":"College of Mathematics and Computer Science, Dali University, Dali 671003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bei","family":"Jiang","sequence":"additional","affiliation":[{"name":"Yunnan Key Laboratory of Screening and Research on Anti-Pathogenic Plant Resources from Western Yunnan, Dali University, Dali 671000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0495-6512","authenticated-orcid":false,"given":"Yi","family":"Li","sequence":"additional","affiliation":[{"name":"College of Mathematics and Computer Science, Dali University, Dali 671003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"696","DOI":"10.1093\/bib\/bbv066","article-title":"Drug\u2013Target Interaction Prediction: Databases, Web Servers and Computational Models","volume":"17","author":"Chen","year":"2016","journal-title":"Brief. 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