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

Journal: Bioinformatics (Oxford, England)
PMID: 38514422

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.

Authors

  • Gabriele Orlando
    Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, La Plaine Campus, Triomflaan.
  • Luis Serrano
    Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Dr Aiguader 88, Barcelona 08003, Spain.
  • Joost Schymkowitz
    Switch Laboratory, VIB-KU Leuven Center for Brain and Disease Research, Herestraat 49, 3000, Leuven, Belgium. joost.schymkowitz@kuleuven.be.
  • Frederic Rousseau
    Switch Laboratory, VIB-KU Leuven Center for Brain and Disease Research, Herestraat 49, 3000, Leuven, Belgium. frederic.rousseau@kuleuven.be.

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