Year: 2025
Type: article
Abstract: Relative binding free energy (ΔΔGbind) predictions have become the main approach to evaluate the potency of a congeneric series of compounds. They are enabled by alchemical transformations coupled to free energy methods, tools that have become essential in drug design. Yet, they are computationally expensive and are limited to small and relatively simple transformations. The ever-increasing size of virtual screening databases demands faster methods to assess virtual hits. Here, we show that the structural robustness of protein-ligand complexes, measured as the quasi-bound free energy (ΔGQB) by Dynamic Undocking (DUck), is well suited to detect outliers in the structure-activity continuum (i.e., activity cliffs), which are particularly challenging for knowledge-based approaches. On congeneric series of HSP90α, CDK2 and BACE-1 inhibitors, we demonstrate that ΔGQB can deliver excellent predictions, despite the local nature of the measurement, in some cases, comparable to the much more computationally demanding alchemical transformation methods. We find that for systems following a one-step dissociation model, ΔGQB actually informs about the free energy of the transition state, which allows us to predict relative binding kinetics and, when the series present relatively constant on-rates, also ΔΔGbind.This work has important implicatinos for drug discovery, as it shows that within a well-defined applicability domain, high-throughput computational dissociation studies can deliver ΔΔGbind predictions that compare well with rigorous alchemical transformation methods.
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,500
OpenAlex ID: W4410537011