Prediction of biomolecule kinetics using physics-based Brownian dynamics to data driven machine learning methods

We review the Brownian dynamics (BD) modeling of biomolecular binding events, with an emphasis on enzyme-substrate interactions in cellular environments. We begin with theoretical foundations of BD and its applications to association and dissociation binding processes in both homogeneous and heterogeneous media. Next, we discuss BD in the context of continuum methods and emerging machine learning (ML) approaches toward predicting binding kinetics in vivo, with an eye toward multiscale modeling perspectives. Finally, we position BD simulations as a bridge to couple atomistic-scale models with dynamic, systems biology-based descriptions of cellular processes as a final frontier in modeling target/enzyme binding processes.