Communication: estimating the initial biasing potential for λ-local-elevation umbrella-sampling (λ-LEUS) simulations...

Area of Science:

• Computational Chemistry
• Chemical Physics
• Molecular Modeling

Background:

• Alchemical free-energy calculations are crucial for understanding molecular interactions.
• Previous methods like λ-dynamics (λD) combined with local-elevation umbrella-sampling (LEUS) improved sampling efficiency.
• However, λ-LEUS required extensive pre-optimization, leading to system-dependent pre-sampling times.

Purpose of the Study:

• To address the lengthy pre-optimization phase in λ-LEUS.
• To develop a faster and more efficient method for alchemical free-energy calculations.
• To reduce the computational cost associated with molecular simulations.

Main Methods:

• Introduction of the slow growth memory guessing (SGMG) approach.
• Utilizing a short slow growth calculation to estimate the potential of mean force.
• Employing the negative of this estimate to initialize the memory in λ-LEUS.

Main Results:

• SGMG significantly reduces the pre-optimization time in λ-LEUS simulations.
• A reduction in pre-optimization time by approximately a factor of four was observed.
• The method was validated on the hydroquinone to benzene perturbation in water system.

Conclusions:

• SGMG offers a substantial improvement in the efficiency of λ-LEUS alchemical free-energy calculations.
• This new approach minimizes non-productive pre-sampling time.
• SGMG makes advanced free-energy calculations more accessible and computationally feasible.