Integrating physics in deep learning algorithms: a force field as a PyTorch module
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- dc.contributor.author Orlando, Gabriele
- dc.contributor.author Serrano Pubull, Luis, 1982-
- dc.contributor.author Schymkowitz, Joost
- dc.contributor.author Rousseau, Frédéric
- dc.date.accessioned 2024-08-01T11:55:05Z
- dc.date.available 2024-08-01T11:55:05Z
- dc.date.issued 2024
- dc.description.abstract Motivation: Deep learning algorithms applied to structural biology often struggle to converge to meaningful solutions when limited data is available, since they are required to learn complex physical rules from examples. State-of-the-art force-fields, however, cannot interface with deep learning algorithms due to their implementation. Results: We present MadraX, a forcefield implemented as a differentiable PyTorch module, able to interact with deep learning algorithms in an end-to-end fashion. Availability and implementation: MadraX documentation, together with tutorials and installation guide, is available at madrax.readthedocs.io.
- dc.description.sponsorship The Switch Laboratory was supported by the Flanders Institute for Biotechnology (VIB, grant no. C0401); KU Leuven; and the Fund for Scientific Research Flanders (FWO, SBO grants S000523N and S000722N). CRG was supported by Spanish Ministry of Science and Innovation through the Centro de Excelencia Severo Ochoa (CEX2020-001049-S, MCIN/AEI /10.13039/501100011033), the Generalitat de Catalunya through the CERCA programme and to the EMBL partnership.
- dc.format.mimetype application/pdf
- dc.identifier.citation Orlando G, Serrano L, Schymkowitz J, Rousseau F. Integrating physics in deep learning algorithms: a force field as a PyTorch module. Bioinformatics. 2024 Mar 29;40(4):btae160. DOI: 10.1093/bioinformatics/btae160
- dc.identifier.doi http://dx.doi.org/10.1093/bioinformatics/btae160
- dc.identifier.issn 1367-4803
- dc.identifier.uri http://hdl.handle.net/10230/60874
- dc.language.iso eng
- dc.publisher Oxford University Press
- dc.relation.ispartof Bioinformatics. 2024 Mar 29;40(4):btae160
- dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/CEX2020-001049-S
- dc.rights © The Author(s) 2024. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.rights.uri http://creativecommons.org/licenses/by/4.0/
- dc.subject.other Biologia computacional
- dc.subject.other Bioinformàtica
- dc.title Integrating physics in deep learning algorithms: a force field as a PyTorch module
- dc.type info:eu-repo/semantics/article
- dc.type.version info:eu-repo/semantics/publishedVersion