  The Targeted Free Energy Perturbation (TFEP) method aims to overcome the
time-consuming and computer-intensive stratification process of standard
methods for estimating the free energy difference between two states. To
achieve this, TFEP uses a mapping function between the high-dimensional
probability densities of these states. The bijectivity and invertibility of
normalizing flow neural networks fulfill the requirements for serving as such a
mapping function. Despite its theoretical potential for free energy
calculations, TFEP has not yet been adopted in practice due to challenges in
entropy correction, limitations in energy-based training, and mode collapse
when learning density functions of larger systems with a high number of degrees
of freedom. In this study, we expand flow-based TFEP to systems with variable
number of atoms in the two states of consideration by exploring the theoretical
basis of entropic contributions of dummy atoms, and validate our reasoning with
analytical derivations for a model system containing coupled particles. We also
extend the TFEP framework to handle systems of hybrid topology, propose
auxiliary additions to improve the TFEP architecture, and demonstrate accurate
predictions of relative free energy differences for large molecular systems.
Our results provide the first practical application of the fast and accurate
deep learning-based TFEP method for biomolecules and introduce it as a viable
free energy estimation method within the context of drug design.

Chemical Physics;Machine Learning