Orlando, GabrieleSerrano Pubull, Luis, 1982-Schymkowitz, JoostRousseau, Frédéric2024-08-012024-08-012024Orlando 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/btae1601367-4803http://hdl.handle.net/10230/60874Motivation: 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.application/pdfeng© 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.Biologia computacionalBioinformàticainfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1093/bioinformatics/btae160info:eu-repo/semantics/openAccess