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By reducing time and cost, machine learning and deep learning can accelerate this laborious discovery process. In a novel approach, BarlowDTI, we utilise the powerful Barlow Twins architecture for feature-extraction while considering the structure of the target protein. Our method achieves state-of-the-art predictive performance against multiple established benchmarks using only one-dimensional input. The use of our hybrid approach of deep learning and gradient boosting machine as the underlying predictor ensures fast and efficient predictions without the need for substantial computational resources. We also propose the use of an influence method to investigate how the model reaches its decision based on individual training samples. By comparing co-crystal structures, we find that BarlowDTI\u00a0effectively exploits catalytically active and stabilising residues, highlighting the model\u2019s ability to generalise from one-dimensional input data. In addition, we further benchmark new baselines against existing methods. Together, these innovations improve the efficiency and effectiveness of drug\u2013target interactions predictions, providing robust tools for accelerating drug development and deepening the understanding of molecular interactions. Therefore, we provide an easy-to-use web interface that can be freely accessed at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/www.bio.nat.tum.de\/oc2\/barlowdti\" ext-link-type=\"uri\">https:\/\/www.bio.nat.tum.de\/oc2\/barlowdti<\/jats:ext-link>.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Scientific contribution<\/jats:title>\n            <jats:p>Our computationally efficient and effective hybrid approach, combining the deep learning model Barlow Twins and gradient boosting machines, outperforms state-of-the-art methods across multiple splits and benchmarks using only one-dimensional input. Furthermore, we advance the field by proposing an influence method that elucidates model decision-making, thereby providing deeper insights into molecular interactions and improving the interpretability of drug-target interactions predictions.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Graphical Abstract<\/jats:title>\n          <\/jats:sec>","DOI":"10.1186\/s13321-025-00952-2","type":"journal-article","created":{"date-parts":[[2025,2,5]],"date-time":"2025-02-05T15:12:40Z","timestamp":1738768360000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Barlow Twins deep neural network for advanced 1D drug\u2013target interaction prediction"],"prefix":"10.1186","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-2415-8810","authenticated-orcid":false,"given":"Maximilian G.","family":"Schuh","sequence":"first","affiliation":[]},{"given":"Davide","family":"Boldini","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0001-9557-663X","authenticated-orcid":false,"given":"Annkathrin I.","family":"Bohne","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9400-906X","authenticated-orcid":false,"given":"Stephan A.","family":"Sieber","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,2,5]]},"reference":[{"key":"952_CR1","volume-title":"Rang & Dale\u2019s pharmacology","author":"HP Rang","year":"2011","unstructured":"Rang HP, Dale MM, Ritter JM, Flower RJ, Henderson G (2011) Rang & Dale\u2019s pharmacology. 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