Integrating physics in deep learning algorithms: a force field as a PyTorch module

Orlando, Gabriele; Serrano Pubull, Luis, 1982-; Schymkowitz, Joost; Rousseau, Frédéric

doi:http://dx.doi.org/10.1093/bioinformatics/btae160

Integrating physics in deep learning algorithms: a force field as a PyTorch module

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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

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.