A Scalable Reinforcement Learning Approach for Screening Large Peptide Libraries for Bioactive Peptide Discovery
Year: 2025
Type: article
Abstract: This dataset contains all inputs and key outputs for the MD simulations in the associated manuscript: final-frame snapshots from all-atom Desmond runs (final-frame-snapshots-fine_grain_Desmond_MDs), starting Maestro system files (Input-initial-configuration-mae_files_Desmond_MD), a tarball with coarse-grained GROMACS coordinates/topologies (gro_and_top_files_coarse_grained_CG_GROMACS.tar), and representative trajectory movies (movies). Files enable inspection and reproduction of the systems and figures as described in the Methods (software versions and parameters as reported there). Please cite this record alongside the manuscript.
Authors: Pandey, Mohit, Foo, Jane, Massah, Shabnam, Alford, Morgan A, Mslati, Hazem, Subbaraj, Gopeshh, Saba, Mira, Gentile, Francesco, Lallous, Nada, Haney, Evan F, Hancock, Robert E. W., Ester, Martin, Cherkasov, Artem
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7084123501
FWCI:
Cites:
Cited by:
Related to: