Computing Absolute Free Energy with Deep Generative Models
This work addresses the need for fast and accurate absolute free energy calculations in fields such as drug design and material engineering, though it appears incremental as it builds on existing methods like deep generative models and the Bennett acceptance ratio.
The authors tackled the problem of computing absolute free energy, which is crucial for applications like drug design and material engineering, by introducing a framework that uses deep generative models to define a reference state with zero free energy, and they demonstrated its effectiveness on discrete and continuous systems, finding that the Bennett acceptance ratio method provides more accurate and efficient estimations than work-based approximations.
Fast and accurate evaluation of free energy has broad applications from drug design to material engineering. Computing the absolute free energy is of particular interest since it allows the assessment of the relative stability between states without the use of intermediates. In this letter, we introduce a general framework for calculating the absolute free energy of a state. A key step of the calculation is the definition of a reference state with tractable deep generative models using locally sampled configurations. The absolute free energy of this reference state is zero by design. The free energy for the state of interest can then be determined as the difference from the reference. We applied this approach to both discrete and continuous systems and demonstrated its effectiveness. It was found that the Bennett acceptance ratio method provides more accurate and efficient free energy estimations than approximate expressions based on work. We anticipate the method presented here to be a valuable strategy for computing free energy differences.