Lambda-ABF-OPES: Faster Convergence with High Accuracy in Alchemical Free Energy Calculations

Area of Science:

• Computational chemistry
• Drug discovery
• Molecular modeling

Background:

• Accurate prediction of binding affinity is crucial for drug development.
• Molecular dynamics (MD) simulations are powerful but computationally expensive for estimating binding free energies.
• Existing methods face challenges in balancing speed and accuracy.

Purpose of the Study:

• To develop a highly efficient hybrid methodology for predicting binding affinities.
• To accelerate therapeutic development through faster computational drug discovery.
• To overcome the computational challenges in estimating absolute binding free energies (ABFEs).

Main Methods:

• Coupling the Lambda-adaptive biasing force (Lambda-ABF) scheme with on-the-fly probability enhanced sampling (OPES).
• Utilizing the AMOEBA polarizable force field for molecular dynamics simulations.
• Implementing a novel hybrid approach to enhance sampling efficiency.

Main Results:

• Achieved up to a 9-fold improvement in sampling efficiency and computational speed.
• Demonstrated converged results at a significantly reduced computational cost compared to standard techniques.
• Validated the efficacy of the hybrid Lambda-ABF and OPES methodology.

Conclusions:

• The developed hybrid methodology offers a substantial advancement in computational drug discovery.
• This approach provides a faster and more cost-effective way to predict binding affinities.
• The findings pave the way for accelerating the identification of potential drug candidates.