ArticleJournal of chemical theory and computation2026
One Blob to Rule Them All: A Universal Dual-Topology Nonequilibrium Framework for Relative and Absolute Binding Free Energies via a Lennard-Jones Blob Reference.
Article in Journal of chemical theory and computation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Single-topology (ST) free energy perturbation (FEP) methods, while accurate for congeneric series, require chemical similarity between ligands, bespoke perturbation networks, and offer no direct route to absolute binding free energies (ABFEs). Here we introduce DT-NE-Alchemy, a dual-topology nonequilibrium (NE) framework that eliminates these constraints through a universal Lennard-Jones (LJ) blob reference state. The LJ blob─a minimal, rigid molecule (as small as a single LJ particle) designed to fit the binding pocket─serves as a common anchor for alchemical transformations. Relative binding free energies between any two ligands are obtained by two-edge transformations Li → blob → Lj, yielding all n(n - 1)/2 pairwise values from only n blob-based calculations. Crucially, ABFEs follow directly as ΔGblob+ΔGblob→Li, converting the long-sought "holy grail" of computational drug design into a practical reality. Applied to 16 MCL-1 ligands spanning indole, benzothiophene, and benzofuran scaffolds, the method achieves Pearson correlations up to 0.88, Kendall τ of ≃0.70, and mean unsigned errors of 1.2-1.5 kcal/mol for 120 relative pairs. The methodology delivers credible, tunable confidence intervals directly from BAR analysis, controllable via switching time τ and trajectory count N without costly replicate simulations. All computational tasks are embarrassingly parallel by design, perfectly aligned with leadership-class HPC systems. DT-NE-Alchemy is automatable and poised to effectively complement ST/FEP for the exploration of chemically diverse libraries and scaffold identification, with the latter remaining the gold standard for lead optimization within strictly congeneric series.
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