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Molecular docking is an <jats:italic>in silico<\/jats:italic> technique that predicts the bound 3D conformations of two molecules, a necessary step for other structure-based methods. Here, we describe version 1.3 of the open-source molecular docking software <jats:sc>Gnina<\/jats:sc>. This release updates the underlying deep learning framework to PyTorch, resulting in more computationally efficient docking and paving the way for seamless integration of other deep learning methods into the docking pipeline. We retrained our CNN scoring functions on the updated CrossDocked2020 v1.3 dataset and introduce knowledge-distilled CNN scoring functions to facilitate high-throughput virtual screening with <jats:sc>Gnina<\/jats:sc>. Furthermore, we add functionality for covalent docking, where an atom of the ligand is covalently bound to an atom of the receptor. This update expands the scope of docking with <jats:sc>Gnina<\/jats:sc> and further positions <jats:sc>Gnina<\/jats:sc> as a user-friendly, open-source molecular docking framework. <jats:sc>Gnina<\/jats:sc> is available at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/gnina\/gnina\" ext-link-type=\"uri\">https:\/\/github.com\/gnina\/gnina<\/jats:ext-link>.<\/jats:p>\n          <jats:p>\n            <jats:bold>Scientific contributions<\/jats:bold>: GNINA 1.3 is an open source\u00a0a molecular docking tool with enhanced support for covalent docking and updated deep learning models for more effective docking and screening.<\/jats:p>","DOI":"10.1186\/s13321-025-00973-x","type":"journal-article","created":{"date-parts":[[2025,3,2]],"date-time":"2025-03-02T20:48:36Z","timestamp":1740948516000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":89,"title":["GNINA 1.3: the next increment in molecular docking with deep learning"],"prefix":"10.1186","volume":"17","author":[{"given":"Andrew T.","family":"McNutt","sequence":"first","affiliation":[]},{"given":"Yanjing","family":"Li","sequence":"additional","affiliation":[]},{"given":"Rocco","family":"Meli","sequence":"additional","affiliation":[]},{"given":"Rishal","family":"Aggarwal","sequence":"additional","affiliation":[]},{"given":"David Ryan","family":"Koes","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,2]]},"reference":[{"issue":"5","key":"973_CR1","doi-asserted-by":"publisher","first-page":"488","DOI":"10.1002\/jcc.540150503","volume":"15","author":"Ruben Abagyan","year":"1994","unstructured":"Abagyan Ruben, Totrov Maxim, Kuznetsov Dmitry (1994) Icm?a new method for protein modeling and design: applications to docking and structure prediction from the distorted native conformation. 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