Generative Machine Learning of Conformational Ensembles of Intrinsically Disordered Proteins: Progress and Opportunities

Irawati Roy, Jagannath Mondal

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DOI: 10.1021/acs.jctc.6c00354
Published: 2026-04-21
Container: Journal of Chemical Theory and Computation
Publisher: American Chemical Society (ACS)
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1021/acs.jctc.6c00354,
  title = {Generative Machine Learning of Conformational Ensembles of Intrinsically Disordered Proteins: Progress and Opportunities},
  author = {Irawati Roy and Jagannath Mondal},
  year = {2026},
  journal = {Journal of Chemical Theory and Computation},
  doi = {10.1021/acs.jctc.6c00354},
  url = {https://doi.org/10.1021/acs.jctc.6c00354}
}

RIS

TY  - JOUR
TI  - Generative Machine Learning of Conformational Ensembles of Intrinsically Disordered Proteins: Progress and Opportunities
AU  - Irawati Roy
AU  - Jagannath Mondal
PY  - 2026
JO  - Journal of Chemical Theory and Computation
DO  - 10.1021/acs.jctc.6c00354
UR  - https://doi.org/10.1021/acs.jctc.6c00354
ER  -

APA

Roy, I., & Mondal, J. (2026). Generative Machine Learning of Conformational Ensembles of Intrinsically Disordered Proteins: Progress and Opportunities. Journal of Chemical Theory and Computation. https://doi.org/10.1021/acs.jctc.6c00354

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crossref · retrieved 2026-09-26T22:08:30.308Z