Free energy calculations in molecular modeling: from classical methods to machine learning.

Area of Science:

• Computational chemistry and molecular modeling.
• Drug discovery and pharmaceutical research.

Background:

• Free energy calculations are vital for molecular design, especially in drug discovery.
• Current methods face challenges with protein flexibility, charged molecules, and slow conformational changes.

Purpose of the Study:

• To review and analyze free energy calculation methodologies.
• To assess current accuracy benchmarks and identify limitations.
• To examine the role of machine learning in addressing these challenges.

Main Methods:

• Comprehensive analysis of foundational and advanced free energy calculation techniques.
• Review of enhanced sampling strategies like replica exchange and metadynamics.
• Integration of machine learning approaches, including neural network potentials and active learning.

Main Results:

• Identified specific failure modes in current free energy calculations.
• Assessed accuracy benchmarks through community challenges and industrial studies.
• Highlighted machine learning's potential to enhance sampling and force field accuracy.

Conclusions:

• Free energy calculations are essential but limited for complex molecular systems.
• Machine learning offers promising solutions for improving efficiency and accuracy.
• Practical guidance on method selection and accuracy expectations is provided.