{"messages":[{"status":"ok","category":"all"}], "collection":[{"title":"PySTARC: GPU-accelerated Brownian dynamics for bimolecular association rate constants","authors":"Ojha, A. A.; Huber, G.; Dutta, S.; Hanson, S. M.","author_corresponding":"Anupam Anand Ojha","author_corresponding_institution":"Flatiron Institute","doi":"10.64898\/2026.09.28.754770","date":"2026-09-29","version":"1","type":"new results","license":"cc_by","category":"biophysics","jatsxml":"https:\/\/www.biorxiv.org\/content\/early\/2026\/09\/29\/2026.09.28.754770.source.xml","abstract":"Drug-target association and dissociation rates often determine in vivo efficacy more than affinity alone. The association rate constant, however, is computationally challenging to predict since the productive encounter is a rare event in a large translational and orientational search space, which lies beyond the reach of conventional atomistic simulations. We present PySTARC (Python Simulation Toolkit for Association Rate Constants), a GPU-accelerated Brownian dynamics engine for estimating bimolecular association rate constants. PySTARC is a Python reimplementation of the BrownDye engine that converges even small reaction probabilities on a single GPU, at a throughput that would otherwise require a large CPU cluster. The current framework resolves reactions between integration steps with a closed-form Brownian bridge, models the internal flexibility of the solute through a coarse-grained bead chain, distributes trajectories across multiple GPUs, checkpoints long runs, monitors convergence, and automates the entire workflow from input structures to rate estimates. PySTARC is validated against protein-ligand and protein-protein complexes spanning five orders of magnitude in the association rate constant, with most estimates within one order of magnitude of experiment.","published":"NA","server":"bioRxiv"}]}