Quantities calculated from molecular simulations are often subject to an initial bias due to unrepresentative starting configurations. Initial data are usually discarded to reduce bias. Chodera’s method for automated truncation point selection [J. Chem. Theory Comput. 2016, 12, 4, 1799–1805] is popular but has not been thoroughly assessed. We reformulate White’s marginal standard error rule to provide a spectrum of truncation point selection heuristics that differ in their treatment of autocorrelation. These include a method effectively equivalent to Chodera’s. We test these methods on ensembles of synthetic time series modeled on free energy change estimates from long absolute binding free energy calculations. Methods that more thoroughly account for autocorrelation often show late and variable truncation times, while methods that less thoroughly account for autocorrelation often show early truncation, relative to the optimal truncation point. This increases variance and bias, respectively. We recommend a method that achieves robust performance across our test sets by balancing these two extremes. None of the methods reliably detected insufficient sampling. All heuristics tested are implemented in the open-source Python package RED (github.com/fjclark/red).

FEgrow is an open-source software package for building congeneric series of compounds in protein binding pockets. For a given ligand core and receptor structure, it employs hybrid machine learning/mol...