[2002.04913] Targeted free energy estimation via learned mappingscontact arXivarXiv Twitter

Free energy perturbation (FEP) was proposed by Zwanzig 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 Free Energy Perturbation, uses a high-dimensional mapping in configuration space to increase 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 (ML) problem in which the mapping is parameterized as a neural network that is optimized so as to increase overlap. We test our method on a fully-periodic solvation system, with a model that respects the inherent permutational and periodic s

2 mentions: @DaniloJRezende@ArgosBiotech
Date: 2020/02/13 11:20

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@DaniloJRezende Estimating free-energy changes in molecular systems is an important problem in biology and materials sciences. We can use normalizing flows here: Targeted free energy estimation via learned mappings t.co/Kd9wCLy5ga Great work lead by Peter Wirnsberger, Andy J. Ballard t.co/rDGWovHj50