Author

Lane William Votapka

University of California San DiegoORCID 0000-0002-0865-5867Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Research Focus:drug response / drug discovery· 2 Assay:protein structure· 1 Approach:simulation· 1 Approach:statistical model· 1

Frequent Co-Authors

Anupam Anand OjhaiD· 2 shared Rommie E. AmaroiD· 2 shared Anson F. Noland· 1 shared Naoya Asada· 1 shared Shiksha DuttaiD· 1 shared Sonya M. HansoniD· 1 shared

Activity by Year

2024-2026 · peak 1/yr

Co-Author Collaboration Network

6 top collaborators · 12 mutual co-author links

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2 shared papersBiophysics

Anupam Anand OjhaORCID

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2 papers

Recent·Most cited

seekrflow: Towards end-to-end automated simulation pipeline with machine-learned force fields for accelerated drug-target kinetic and thermodynamic predictions

Anupam Anand Ojha, Lane William Votapka, Shiksha Dutta, Anson F. Noland, Sonya M. Hanson, Rommie E. Amaro

seekrflow automates drug-target kinetic and thermodynamic predictions using a multiscale milestoning simulation pipeline, achieving accurate results with reduced manual intervention and computational cost.

Journal of Chemical Theory and Computation · Jun 12, 2026Peer-reviewedBiophysics

Prediction of Threonine-Tyrosine Kinase Receptor-LigandUnbinding Kinetics with Multiscale Milestoning andMetadynamics

Lane William Votapka, Anupam Anand Ojha, Naoya Asada, Rommie E. Amaro

The paper predicts drug residence times for threonine-tyrosine kinase inhibitors using multiscale milestoning and metadynamics simulations.

The Journal of Physical Chemistry Letters · Oct 11, 2024Peer-reviewedBiophysics