ALCHEMD: Bridging Accessibility and Accuracy in Automated Relative Binding Free Energy Workflows

Alchemical free energy perturbation (FEP) has emerged as one of the most accurate computational methods for predicting drug-protein binding affinity. However, its adoption in drug discovery workflows has been limited by two significant challenges: excessive computational requirements that demand access to HPC clusters, and high technical complexity that restricts its use to a small group of experts. Here we present ALCHEMD, a fully automated open-source platform that enables relative binding free energy (FEP-RBFE) calculations on desktop workstations, achieving 10-20 drug-protein binding predictions daily on commodity GPUs. ALCHEMD addresses these challenges through an integrated approach, including (a) intelligent preprocessing with automated reference ligand selection and nonstandard residue parametrization; (b) Common Structure Mapping algorithm that leverages 3D structural information to resolve symmetric mapping ambiguities; (c) Combined-Structure FEP methodology that introduces a new thermodynamic cycle for smoother alchemical transformations; and (d) Convergence-Adaptive Roundtrip algorithm that enables automated enhanced adaptive sampling with dynamic resource allocation. In benchmark tests, ALCHEMD achieves comparable accuracy (MUE = 0.86 kcal/mol, R² = 0.60, τ = 0.56) while requiring only 29.3 ns average simulation time per ligand pair─4-8 fold faster than conventional protocols. The platform features dual graphical and command-line interfaces, broadening accessibility to the drug discovery community.