State Function-Based Correction: A Simple and Efficient Free-Energy Correction Algorithm for Large-Scale Relative Binding Free-Energy Calculations

作者：Runduo Liu, Yijun Lai, Yufen Yao, Wanyi Huang, Yilin Zhong, Hai‐Bin Luo, Zhe Li · 发表于：The Journal of Physical Chemistry Letters · 年份：2025 · DOI：10.1021/acs.jpclett.5c01119 · 被引用次数：3 · 研究领域：Machine Learning in Materials Science、Protein Structure and Dynamics、Computational Drug Discovery Methods

Free-energy perturbation-based relative binding free-energy (FEP-RBFE) calculations have become an important tool in drug discovery, but inherent computational errors require corrections based on fundamental physical principles to improve prediction accuracy. Traditional correction methods enforce physical consistency by identifying cycles in perturbation graphs, but their computational cost grows exponentially with network size due to the combinatorial explosion of cycles. This severely limits their applicability to modern drug discovery, where large-scale FEP-RBFE screens involving hundreds to thousands of ligands are increasingly common. We present an efficient and straightforward State Function-based Correction (SFC) algorithm, which leverages the state function property of free energy without requiring cycle identification. This eliminates computational bottlenecks, with the computational cost scaling as O ( P × N ), where P is the number of edges in the perturbation graph and N is the number of molecules. In contrast to graph-based methods such as weighted cycle closure (WCC), SFC maintains consistent computational efficiency across increasing graph sizes, enabling the efficient handling of large perturbation networks with up to 50 000 molecules or even more─useful for high-throughput FEP-RBFE applications. Furthermore, SFC incorporates uncertainty-aware weighting to further enhance correction performance. These advantages position SFC as an efficient RBFE correction me...