Precise Binding Free Energy Calculations for Multiple Molecules Using an Optimal Measurement Network of Pairwise...

Area of Science:

• Computational chemistry
• Drug discovery
• Molecular modeling

Background:

• Alchemical binding free energy (BFE) calculations are crucial for predicting molecular binding affinities in drug discovery.
• Statistical variance in BFE calculations can compromise the reliability of binding affinity predictions and molecule ranking.
• Existing computational resource allocation methods may not be optimal for minimizing this variance.

Purpose of the Study:

• To develop a computational method that enhances the statistical precision of alchemical BFE calculations.
• To improve the reliability of binding affinity predictions for drug candidate prioritization.
• To reduce uncertainty in benchmarking the predictive accuracy of BFE methods.

Main Methods:

• Introduced Network Binding Free Energy (NetBFE), an adaptive computational approach for BFE calculations.
• NetBFE dynamically re-allocates computational resources across multiple BFE calculations in iterative steps.
• Resource allocation is optimized based on estimated statistical variances from preceding iterations.

Main Results:

• NetBFE rapidly converges to optimal resource allocation within a small number of iterations (≤5).
• NetBFE significantly reduces statistical variance in BFE estimates by approximately 50% compared to prior methods.
• This improvement is achieved at the same total computational cost.

Conclusions:

• NetBFE offers a substantial improvement in the statistical precision of alchemical BFE calculations.
• The adaptive resource allocation strategy enhances the reliability of in silico binding affinity predictions.
• NetBFE provides a more robust tool for guiding drug discovery and development efforts.