Multiscale End-point Screening with Extended Tight-binding Hamiltonians

Background: Extended tight-binding (xTB) methods offer a computationally efficient alternative to classical force fields and ab initio quantum methods in modeling molecular systems. In the context of end-point free energy calculations, integrating xTB with implicit solvation models provides a promising route for enhanced accuracy. However, systematic benchmarking of xTB-based protocols remains limited, particularly in diverse host-guest systems. Methods: We investigated the integration of xTB Hamiltonians (GFN0, GFN1, and GFN2) with post-simulation implicit-solvent models [Poisson−Boltzmann (PB), generalized Born (GB), and the most recent CPCM-X] for end-point free energy calculations. A total of over 250 host-guest complexes were used, covering cucurbiturils, octa acids, and pillararenes. Both single-trajectory and three-trajectory sampling protocols were applied. Entropic contributions were estimated via MM-based normal mode analysis and xTB-based statistical approximations. We evaluated predictive performance using Kendall τ, Pearson r, and predictive index. Results: The three-trajectory protocol consistently outperformed the single-trajectory counterpart across Hamiltonians and solvent models. Among all configurations, the GFN2-xTB/PB combination showed the best predictive accuracy, although it fell short of the top-performing MM/GBOBCSA ΔG method. Notably, in challenging systems like sulfur-substituted pillararenes, xTB methods exhibited superior performance, whereas MM/GBSA failed due to inadequate error cancellation. The use of CPCM-X did not further enhance accuracy, possibly due to unsuccessful error cancellation. Conclusions: While MM/GBSA remains the most robust protocol for general use, the GFN2-xTB/PB ΔH method emerges as a viable alternative for cases where MM-based methods perform poorly. These findings highlight the value of xTB-based multiscale approaches for receptor-ligand binding, especially in complex or chemically diverse systems.