AdaptivePy: A Unified Python Framework for Adaptive Sampling in Molecular Dynamics

Abstract

Adaptive sampling accelerates the exploration of conformational space in molecular dynamics (MD) simulations by repeatedly analyzing the accumulated trajectories and seeding a new round of simulations from informative configurations. A growing collection of adaptive sampling policies has been proposed, each built around a particular notion of what makes a configuration informative, yet these methods are scattered across separate and often incompatible implementations, which complicates their systematic comparison and their combined use in meta-adaptive sampling schemes. Here, we present AdaptivePy, a compact and extensible Python framework that implements nine seed-selection policies behind a single configuration-driven interface, spanning simple population-based baselines, several established machine-learning and geometry-based methods, and two ensemble or meta-sampling policies introduced in this work. We show that the shared implementation reproduces the characteristic selection behavior of each policy on a series of analytic benchmark landscapes. We also introduce a new adaptive sampling scheme that employs TS-DAR, a deep learning framework originally designed to identify transition states, as an acquisition criterion that drives the discovery of an entire multibasin landscape starting from a single basin. We further demonstrate that the common interface enables meta-adaptive sampling policies, which aggregate the rankings of several policies into a single set of seeds. We apply all nine policies to explicit-solvent simulations of alanine dipeptide and the miniprotein chignolin. The policy that performs best depends on the system, on the performance metric, and on the amount of accumulated sampling, justifying the use of meta-policies that combine different algorithms. AdaptivePy thereby provides a unified testbed for the adoption, benchmarking, and continued development of adaptive sampling methods for biomolecular MD simulations.