DeepPath: overcoming data scarcity for protein transition pathway prediction using physics-based deep learning
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Abstract
DeepPath combines generative learning with classical force fields to discover physically realistic protein transition pathways.
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Cites
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Flagged first, then by citations
- Authors
- Pang, Y. T., Yang, L., Kuo, K. M., & Gumbart, J. C.
- Affiliations
- Georgia Institute of Technology (US)
- Corresponding author
- Gumbart, James C.
- Venue
- Chemical Science
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2026
- Volume
- 17
- Issue
- 24
- Pages
- 12055-12073
- ISSN
- 2041-6520 (ISSN-L)
- 2041-6539
- PMC
- PMC11888466
- OpenAlex
- W4408086132
- Cited by
- 13
- References
- 15
- Citations by year
- 2026: 10 · 2025: 3
- FWCI
- 20.67
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Chemical Science (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed Central (submittedVersion, cc-by-nc)
- Funders
- National Institute of General Medical Sciences (R01-GM148586)
- National Science Foundation (2138259, 2138286, 2138307, 2137603, 2138296, OAC-1818253, 1828187)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Receptor Mechanisms and Signaling
- Keywords
- physics-guided deep learning, protein structural dynamics, data scarcity, protein structure prediction, protein intermediates
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