Author

J. K. Cava

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Biophysics· 2

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Approach:deep learning· 2 Assay:multiome· 1 Research Focus:protein folding & dynamics· 1 Approach:statistical model· 1 Approach:transformer / foundation model· 1

Frequent Co-Authors

A. Singharoy· 2 shared J. Vant· 2 shared A. Shukla· 1 shared C. Gupta· 1 shared D. Sarkar· 1 shared E. A. Wilson· 1 shared J. Miratsky· 1 shared N. Ho· 1 shared

Activity by Year

2020-2022 · peak 1/yr

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2 shared papersBiophysics

A. Singharoy

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2 papers

Recent·Most cited

Learning Free Energy Pathways through Reinforcement Learning of Adaptive Steered Molecular Dynamics

N. Ho, J. K. Cava, J. Vant, A. Shukla, J. Miratsky, P. Turaga, R. Maciejewski, A. Singharoy

This paper develops a reinforcement learning framework to compute low free energy pathways between states using adaptive steered molecular dynamics and robotics planning.

bioRxiv · Oct 4, 2022 · PreprintBiophysics

Mind reading of the proteins: Deep-learning to forecast molecular dynamics

C. Gupta, J. K. Cava, D. Sarkar, E. A. Wilson, J. Vant, S. Murray, A. Singharoy, S. K. Karmaker

This paper uses deep learning to predict molecular dynamics from simulation data, finding that non-equilibrium MD is easier to train and performance improves with atom independence assumptions.

bioRxiv · Jul 28, 2020 · PreprintBiophysics