seekrflow: Towards end-to-end automated simulation pipeline with machine-learned force fields for accelerated drug-target kinetic and thermodynamic predictions Article Swipe
Anupam Anand Ojha
,
Lane Votapka
,
Suman Dutta
,
Anson F. Noland
,
Sonya M. Hanson
,
Rommie E. Amaro
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.08.13.669965
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.08.13.669965
Accurate prediction of drug-target binding and unbinding kinetics and thermodynamics is essential for guiding drug discovery and lead optimization. However, traditional atomistic simulations are often too computationally expensive to capture rare events that govern ligand (un)binding. Several enhanced sampling methods exist to overcome these limitations, but they require extensive manual intervention and introduce variability and artifacts in free energy and kinetic estimates that limit high-throughput scalability. The present work introduces seekrflow, an automated multiscale milestoning simulation pipeline that streamlines the entire workflow from a single receptor-ligand input structure to kinetic and thermodynamic predictions in a single step. This integrated approach minimizes manual intervention, reduces computational overhead, and enhances the reproducibility and accuracy of kinetic and thermodynamic predictions. The accuracy and efficiency of the pipeline is demonstrated on multiple receptor-ligand complexes, including inhibitors of heat shock protein 90, threonine-tyrosine kinase, and the trypsin protein, with predicted kinetic parameters closely matching experimental estimates. seekrflow establishes a new benchmark for automated and high-throughput physics-based predictions of kinetics and thermodynamics.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1101/2025.08.13.669965
- https://www.biorxiv.org/content/biorxiv/early/2025/08/17/2025.08.13.669965.full.pdf
- OA Status
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- References
- 172
- Related Works
- 10
- OpenAlex ID
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seekrflow: Towards end-to-end automated simulation pipeline with machine-learned force fields for accelerated drug-target kinetic and thermodynamic predictionsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-08-16Full publication date if available
- Authors
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Anupam Anand Ojha, Lane Votapka, Suman Dutta, Anson F. Noland, Sonya M. Hanson, Rommie E. AmaroList of authors in order
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https://doi.org/10.1101/2025.08.13.669965Publisher landing page
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https://www.biorxiv.org/content/biorxiv/early/2025/08/17/2025.08.13.669965.full.pdfDirect link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://www.biorxiv.org/content/biorxiv/early/2025/08/17/2025.08.13.669965.full.pdfDirect OA link when available
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0Total citation count in OpenAlex
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| publication_date | 2025-08-16 |
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