Paper List

期刊: ArXiv Preprint
发布日期: 2026-03-12
Computational ChemistryBioinformatics

Binding Free Energies without Alchemy

Eshelman School of Pharmacy, University of North Carolina at Chapel Hill | Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill

Michael Brocidiacono, Brandon Novy, Rishabh Dey, Konstantin I. Popov, Alexander Tropsha
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IN SHORT: This paper addresses the core bottleneck of computational expense in Absolute Binding Free Energy calculations by eliminating the need for numerous alchemical intermediate simulations, reducing per-ligand simulation cost by up to 26x.

核心创新

  • Methodology Introduces Direct Binding Free Energy (DBFE), a novel end-state ABFE method that requires only three simulations (receptor-only, ligand-only, and complex) without alchemical intermediates.
  • Methodology Employs a combinatorial sampling strategy using KD-trees for fast steric clash detection, enabling efficient estimation of conformational entropy from precomputed simulations.
  • Methodology Demonstrates a 26x reduction in per-ligand simulation cost compared to double decoupling methods in virtual screening contexts through amortization of receptor simulations.

主要结论

  • DBFE achieved Pearson correlation r=0.58 on host-guest systems, outperforming OBC2 double decoupling (r=0.48) and demonstrating the importance of conformational entropy correction for these systems.
  • On protein-ligand benchmarks, DBFE achieved r=0.65, slightly worse than OBC2 MM/GBSA (r=0.71), suggesting conformational entropy estimation introduces noise for complex protein systems.
  • The performance gap between implicit solvent methods (DBFE/OBC2 DD r=0.65-0.73) and explicit solvent TIP3P DD (r=0.88) indicates that improving implicit solvent models would yield greater accuracy gains than improving free energy estimators.
研究空白： Current ABFE methods like double decoupling are computationally prohibitive for virtual screening due to requiring many alchemical intermediate simulations (typically 30+ lambda windows per compound), creating a throughput bottleneck.

摘要: Absolute Binding Free Energy (ABFE) methods are among the most accurate computational techniques for predicting protein-ligand binding affinities, but their utility is limited by the need for many simulations of alchemically modified intermediate states. We propose Direct Binding Free Energy (DBFE), an end-state ABFE method in implicit solvent that requires no alchemical intermediates. DBFE outperforms OBC2 double decoupling on a host-guest benchmark and performs comparably to OBC2 MM/GBSA on a protein-ligand benchmark. Since receptor and ligand simulations can be precomputed and amortized across compounds, DBFE requires only one complex simulation per ligand compared to the many lambda windows needed for double decoupling, making it a promising candidate for virtual screening workflows. We publicly release the code for this method at https://github.com/molecularmodelinglab/dbfe.

代码