{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T09:55:00Z","timestamp":1790589300281,"version":"4.1.0"},"reference-count":35,"publisher":"Walter de Gruyter GmbH","license":[{"start":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T00:00:00Z","timestamp":1790553600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"CNPq \u2013 Brazilian National Research Council","award":["305235\/2021-4"],"award-info":[{"award-number":["305235\/2021-4"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    The computational burden of molecular docking becomes prohibitive when receptor flexibility is explicitly considered, despite the accuracy gained in virtual screening workflows. To mitigate this limitation, we introduce a three-dimensional deep neural network designed to estimate binding energies before exhaustive docking across the complete fully flexible receptor ensemble. We target the InhA enzyme, a validated target of\n                    <jats:italic>Mycobacterium tuberculosis<\/jats:italic>\n                    , through the use of a fully flexible receptor model (with 20,000 poses) generated by computational simulation. The model leverages volumetric representations of electrostatic charge distributions in the InhA binding pocket after positioning a candidate ligand. Training was conducted with a curated collection of 60 ZINC compounds previously ranked as promising ligands. The approach reproduces docking trends with a Pearson correlation close to 0.70 and a Spearman correlation above 0.66, and processes more than 150,000 receptor\u2013ligand conformations in approximately 25\u202fmin. These results highlight the feasibility of employing a deep neural network model as a pre-filtering mechanism within a curated ligand set and a specific fully flexible receptor workflow, aiming to reduce the number of docking simulations required, thereby accelerating rational drug discovery against tuberculosis.\n                  <\/jats:p>","DOI":"10.1515\/jib-2025-0072","type":"journal-article","created":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T08:56:12Z","timestamp":1790585772000},"source":"Crossref","is-referenced-by-count":0,"title":["Accelerating ligand screening for\u00a0tuberculosis: predicting binding energies with\u00a0a 3D deep neural network"],"prefix":"10.1515","author":[{"given":"Duncan Dubugras A.","family":"Ruiz","sequence":"first","affiliation":[{"name":"Business Intelligence and Machine Learning Research Group \u2013 GPIN, Graduate Program in Computer Science \u2013 PPGCC, Pontifical Catholic University of Porto Alegre \u2013 PUCRS , Porto Alegre , RS , Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Renata","family":"De Paris","sequence":"additional","affiliation":[{"name":"Kunumi Institute , Porto Alegre , RS , Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christian Vahl","family":"Quevedo","sequence":"additional","affiliation":[{"name":"Kunumi Institute , Porto Alegre , RS , Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Osmar","family":"Norberto de Souza","sequence":"additional","affiliation":[{"name":"Laboratory for Bioinformatics, Modeling and Simulation of Biosystems LABIO , Porto Alegre , RS , Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","published-online":{"date-parts":[[2026,9,28]]},"reference":[{"key":"2026092808560741830_j_jib-2025-0072_ref_001","unstructured":"W.H.O. 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J Integr Bioinf 2018;15:20170041. https:\/\/doi.org\/10.1515\/jib-2017-0041.","DOI":"10.1515\/jib-2017-0041"},{"key":"2026092808560741830_j_jib-2025-0072_ref_007","doi-asserted-by":"crossref","unstructured":"De Paris, R, Quevedo, CV, Ruiz, DD, Gargano, F, Noberto De Souza, O. A selective method for optimizing ensemble docking-based experiments on an InhA fully-flexible receptor model. BMC Bioinf 2018;19:235\u201350. https:\/\/doi.org\/10.1186\/s12859-018-2222-2.","DOI":"10.1186\/s12859-018-2222-2"},{"key":"2026092808560741830_j_jib-2025-0072_ref_008","doi-asserted-by":"crossref","unstructured":"De Paris, R, Quevedo, CV, Ruiz, DD, Noberto De Souza, O. An effective approach for clustering InhA molecular dynamics trajectory using substrate-binding cavity features. 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