Doing More with Less: Accurate and Scalable Ligand Free Energy Calculations by Focusing on the Binding Site

TL;DR

QligFEP is a new tool that makes drug binding predictions faster and cheaper by focusing simulations on the binding site.

Contribution

QligFEP v2.1.0 introduces a scalable, accurate, and computationally efficient workflow for ligand free energy calculations.

Findings

QligFEP achieves comparable accuracy to commercial tools while using fewer computational resources.

Each perturbation leg simulates around 6250 atoms and completes in under 2 hours on standard clusters.

The method is cost-effective, with simulations costing less than $1 on AWS spot instances.

Abstract

Predicting how chemical modifications affect drug binding
is central
to rational drug design. Free energy perturbation (FEP) calculations
provide accurate estimates of these binding affinity changes, but
existing methods often require substantial computational resources
and expert knowledge. Here, we present QligFEP v2.1.0, a flexible
open-source workflow based on a graphical and command-line interface
for calculating relative binding free energies using spherical boundary
conditions, which dramatically reduces simulation system size by confining
simulations to a focused region around the binding site. QligFEP features
a configurable restraint algorithm that automatically handles diverse
chemical transformations, streamlined setup procedures, and enhanced
analysis tools. We validated the method using industry benchmarks
comprising 16 protein targets and 639 ligand transformations. Statistical
analysis demonstrates that QligFEP achieves comparable accuracy to
established commercial and open-source alternatives while requiring
only a fraction of the computational resources. The perturbation protocol
simulates ∼6250 atoms per perturbation leg and completes transformation
replicates in under 2 h on standard computational clusters. Unlike
full-system simulations, QligFEP’s modest computational requirements
make FEP accessible for less than $1 on current AWS spot instances.
The combination of accuracy, flexibility, and computational efficiency
positions QligFEP as a practical solution for accelerating compound
optimization in drug discovery, making rigorous binding affinity predictions
accessible for large scale applications and to research groups with
limited computational infrastructure.

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