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

2025/12/02 by David Alencar Araripe, Alejandro Díaz‐Holguín, Antti Poso +4 · 1 voice
Biochemistry, Genetics and Molecular Biology · Computer Science · #Computational Drug Discovery Methods #Protein Degradation and Inhibitors #Protein Structure and Dynamics

paper · doi:10.26434/chemrxiv-2025-x3r3z-v3

openalex publication_date 2025/12/02 · openalex created_date 2025/12/03 · openalex updated_date 2026/07/22

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, an automated open-source platform for calculating relative binding free energies using spherical boundary conditions (SBC), which dramatically reduces computational cost 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 hours on standard computational clusters, which is substantially faster than traditional full-system simulations, and currently would cost less than 1 on current AWS spot instances. The combination of accuracy, automation, 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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