Targeted free energy estimation via learned mappings

P Wirnsberger and AJ Ballard and G Papamakarios and S Abercrombie and S Racaniere and A Pritzel and DJ Rezende and C Blundell, JOURNAL OF CHEMICAL PHYSICS, 153, 144112 (2020).

DOI: 10.1063/5.0018903

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.

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