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Assessing the Accuracy and Efficiency of Free Energy Differences Obtained from Reweighted Flow-Based Probabilistic
Edgar Olehnovics1, Yifei Michelle Liu2, Nada Mehio3
1Thomas Young Centre and Department of Chemical Engineering, University College London, London WC1E 7JE, U.K.
Journal of Chemical Theory and Computation
|July 10, 2024
Summary
Targeted free energy perturbation (TFEP) uses machine learning to create invertible maps for faster free energy calculations. Bennett acceptance ratio (BAR) and multistate Bennett acceptance ratio (MBAR) methods prove data-efficient and robust for reweighting these maps.
Area of Science:
- Computational Chemistry
- Machine Learning
- Statistical Mechanics
Background:
- Calculating free energy differences between metastable states is computationally demanding, often requiring numerous intermediate states.
- Targeted free energy perturbation (TFEP) offers a computationally efficient alternative by directly connecting states with invertible maps.
- Probabilistic generative models (PGMs) utilizing normalizing flows can facilitate the training of these invertible maps for TFEP.
Purpose of the Study:
- To assess the accuracy, convergence rate, and data efficiency of different free energy estimators for reweighting PGMs.
- To compare exponential averaging, Bennett acceptance ratio (BAR), and multistate Bennett acceptance ratio (MBAR) in this context.
- To evaluate these methods using limited molecular dynamics data from end-states.
Main Methods:
- Trained PGMs using maximum likelihood on limited molecular dynamics data from end-states.
- Employed TFEP with normalizing flow-based invertible maps.
- Assessed free energy estimators including exponential averaging, BAR, and MBAR for reweighting.
- Conducted comparisons on alanine dipeptide and ibuprofen conformational ensembles.
Main Results:
- BAR and MBAR demonstrated superior data efficiency and robustness compared to exponential averaging.
- These estimators remained effective even with significant model overfitting in the generated maps.
- The study provides a quantitative comparison of reweighting strategies for ML-driven free energy calculations.
Conclusions:
- BAR and MBAR are highly suitable for reweighting in ML-based TFEP calculations, offering efficiency and accuracy.
- The findings support the use of these methods for complex systems, paving the way for advanced computational chemistry.
- This work establishes a foundation for deploying accurate and efficient ML-based free energy calculation methods.
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