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Integrating physics in deep learning algorithms: a force field as a PyTorch module
Open accessPublished version of a preprint
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.
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Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
TorchMD: A Deep Learning Framework for Molecular Simulations
Doerr, S., Majewski, M., Pérez, A., Krämer, A., Clementi, C., Noe, F., Giorgino, T., & De Fabritiis, G. (2021) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.0c01343 · Cited by 230
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Metadata
- Authors
- Orlando, G., Serrano, L., Schymkowitz, J., & Rousseau, F.
- Affiliations
- VIB-KU Leuven Center for Brain & Disease Research (BE)
- Institució Catalana de Recerca i Estudis Avançats (ES)
- Switch Laboratory, VIB Center for Brain and Disease Research, VIB , Leuven 3000, Belgium
- Corresponding author
- Schymkowitz, Joost
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2024-03-21
- Volume
- 40
- Issue
- 4
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC11007235
- OpenAlex
- W4393094737
- Cited by
- 7
- References
- 2
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 1
- FWCI
- 0.95
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Repositori digital de la UPF (Universitat Pompeu Fabra) (submittedVersion, cc-by)
- Lirias (submittedVersion, cc-by)
- Funders
- Flanders Institute for Biotechnology
- Fund for Scientific Research Flanders (S000523N, S000722N)
- Spanish Ministry of Science and Innovation through the Centro de Excelencia Severo Ochoa (CEX2020-001049-S, MCIN/AEI /10.13039/501100011033)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- force fields, deep learning integration, physics-informed machine learning, structural biology, protein structure prediction, end-to-end learning
- MeSH terms
- Deep Learning, Algorithms, Documentation
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