2026-04-17T09:42:23Zhttps://recercat.cat/oai/request

oai:recercat.cat:10230/608742025-12-12T02:33:51Zcom_2072_6col_2072_452952

00925njm 22002777a 4500 dc Orlando, Gabriele author Serrano Pubull, Luis, 1982- author Schymkowitz, Joost author Rousseau, Frédéric author 2024-08-01T11:55:05Z 2024-08-01T11:55:05Z 2024 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. Biologia computacional Bioinformàtica Integrating physics in deep learning algorithms: a force field as a PyTorch module