Related Experiment Video
Updated: Dec 4, 2025

Förster Resonance Energy Transfer Mapping: A New Methodology to Elucidate Global Structural Features
Published on: March 16, 2022
Targeted free energy estimation via learned mappings
Peter Wirnsberger1, Andrew J Ballard1, George Papamakarios1
1DeepMind, London, United Kingdom.
Abstract:
Free energy perturbation (FEP) was proposed by Zwanzig [J. Chem. Phys. 22, 1420 (1954)] more than six decades ago as a method to estimate free energy differences and has since inspired a huge body of related methods that use it as an integral building block. Being an importance sampling based estimator, however, FEP suffers from a severe limitation: the requirement of sufficient overlap between distributions. One strategy to mitigate this problem, called Targeted FEP, uses a high-dimensional mapping in configuration space to increase the overlap of the underlying distributions. Despite its potential, this method has attracted only limited attention due to the formidable challenge of formulating a tractable mapping. Here, we cast Targeted FEP as a machine learning problem in which the mapping is parameterized as a neural network that is optimized so as to increase the overlap. We develop a new model architecture that respects permutational and periodic symmetries often encountered in atomistic simulations and test our method on a fully periodic solvation system. We demonstrate that our method leads to a substantial variance reduction in free energy estimates when compared against baselines, without requiring any additional data.
Related Concept Videos
Free Energy
Calculating Standard Free Energy Changes
Gibbs Free Energy
Gibbs Free Energy and Thermodynamic Favorability
An Introduction to Free Energy
Free Energy Changes for Nonstandard States

