{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T13:17:37Z","timestamp":1770902257801,"version":"3.50.1"},"reference-count":66,"publisher":"Oxford University Press (OUP)","issue":"8","license":[{"start":{"date-parts":[[2025,8,19]],"date-time":"2025-08-19T00:00:00Z","timestamp":1755561600000},"content-version":"vor","delay-in-days":18,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"HPC resources of CRIANN","award":["262402"],"award-info":[{"award-number":["262402"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,8,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>The field of protein\u2013ligand binding affinity prediction continues to face significant challenges. While deep learning (DL) models can leverage 3D structural information of protein\u2013ligand complexes, they perform well only on heavily biased test sets containing information leaked from training sets. This lack of generalization arises from the limited availability of training data and the models\u2019 inability to effectively learn from protein\u2013ligand interactions. Since these interactions are inherently time-dependent, molecular dynamics (MD) simulations offer a potential solution by incorporating conformational sampling and providing interaction rich information.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We have developed MDbind, a dataset comprising 63\u2009000 simulations of protein\u2013ligand interactions, along with novel neural networks capable of learning from these simulations to predict binding affinity. By utilizing MD as data augmentation, our models achieved state-of-the-art performance on the PDBbind v.2016 core set and an external test set, the free energy perturbation (FEP) dataset. Additionally, when trained on the full MD simulations, the models demonstrated less biased predictions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The code for neural networks is available at https:\/\/github.com\/ICOA-SBC\/MD_DL_BA. The models, the results and the training\/validation\/test sets are available for download at https:\/\/zenodo.org\/records\/10390550. The MDbind trajectories are being transferred to the MDDB: https:\/\/mdposit.mddbr.eu\/#\/browse?search=MDBind .<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaf429","type":"journal-article","created":{"date-parts":[[2025,8,17]],"date-time":"2025-08-17T11:24:14Z","timestamp":1755429854000},"source":"Crossref","is-referenced-by-count":7,"title":["Spatio-temporal learning from molecular dynamics simulations for protein\u2013ligand binding affinity prediction"],"prefix":"10.1093","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-8363-5897","authenticated-orcid":false,"given":"Pierre-Yves","family":"Libouban","sequence":"first","affiliation":[{"name":"Institute of Organic and Analytical Chemistry (ICOA), UMR7311, Universit\u00e9 d\u2019Orl\u00e9ans, CNRS, P\u00f4le de chimie rue de Chartres , 45067 Orl\u00e9ans Cedex 2,","place":["France"]}]},{"given":"Camille","family":"Parisel","sequence":"additional","affiliation":[{"name":"Institute for Development and Resources in Intensive Scientific Computing (IDRIS), CNRS, Rue John Von Neumann , 91403 Orsay Cedex,","place":["France"]}]},{"given":"Maxime","family":"Song","sequence":"additional","affiliation":[{"name":"Institute for Development and Resources in Intensive Scientific Computing (IDRIS), CNRS, Rue John Von Neumann , 91403 Orsay Cedex,","place":["France"]}]},{"given":"Samia","family":"Aci-S\u00e8che","sequence":"additional","affiliation":[{"name":"Institute of Organic and Analytical Chemistry (ICOA), UMR7311, Universit\u00e9 d\u2019Orl\u00e9ans, CNRS, P\u00f4le de chimie rue de Chartres , 45067 Orl\u00e9ans Cedex 2,","place":["France"]}]},{"given":"Jose C","family":"G\u00f3mez-Tamayo","sequence":"additional","affiliation":[{"name":"Computational Chemistry, Janssen Research & Development, Janssen Pharmaceutica N. 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