MLPfold: Identification of transition state ensembles in molecular dynamics simulations using machine learning

Molecular dynamics simulations generate a large amount of raw data, which often require computationally expensive analysis to extract important information. Here, we propose a novel method, MLPfold, to identify the transition state ensemble of a system through an automated labeling process and supervised learning using a simple MLP. This seeks to replicate the conventional Pfold calculation but without requiring the running of any additional simulations. MLPfold was tested on numerous model potentials and Brownian dynamics simulation of the Ubiquitin hairpin and shows promise in predicting committor probabilities and identifying transition states.