The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Integrating physics in deep learning algorithms: A force field as a PyTorch module
Open accessPreprint — published as 10.1093/bioinformatics/btae160 · Paper page
Abstract
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. We present MadraX, a forcefield implemented as a differentiable PyTorch module, able to interact with deep learning algorithms in an end-to-end fashion. MadraX 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
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Research works printed but not linked: 15
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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)
- Corresponding author
- Schymkowitz, Joost
- Venue
- Bioinformatics
- Publisher
- openRxiv
- Published
- 2023-01-16
- Volume
- 40
- Issue
- 4
- ISSN
- 1367-4803 (ISSN-L)
- PMC
- PMC11007235
- OpenAlex
- W4393094737
- Cited by
- 7
- References
- 1
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 1
- FWCI
- 0.95
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- 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
- Centres de Recerca de Catalunya (501100011033)
- Generalitat de Catalunya (501100011033)
- Fonds Wetenschappelijk Onderzoek (S000722N)
- KU Leuven
- Vlaams Instituut voor Biotechnologie (C0401)
- Ministerio de Ciencia e Innovación (.13039/501100011033, 501100011033, CEX2020-001049-S)
- Vlaamse regering
- Agencia Estatal de Investigación (501100011033, 13039, .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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