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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, R 2 = 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.
Liu et al. (Wed,) studied this question.