Molecular dynamics (MD) simulations provide valuable insights into the nature of these interactions, e.g., by considering an ensemble of bound conformations to generate thermodynamically accurate estimates of various energy contributions . This is usually done by calculating the statistical properties of systems in thermodynamic equilibrium and estimating the time spent in the various microstates. Therefore, MD has the potential to connect the chemical world to physical observables, aiding in the determination of state variables (free energy, enthalpy, entropy, …), kinetics, and the exploration of biomolecular mechanisms driven by rare events . Numerous studies have illuminated the remarkable performance of MD simulations in predicting experimental outcomes, showcasing their transformative potential to accelerate and economize the drug discovery process. For instance, the ligand gaussian accelerated MD (LGMD) method, an enhanced sampling technique pioneered by Miao et al. , was employed to forecast the binding affinity of nirmatrelvir with the coronavirus 3C-like protease, yielding predictions in striking concordance with experimental observations , . Likewise, Wolf et al. harnessed the power of Langevin simulations an extended MD approach that delves into the intricate low-frequency motions governing large conformational shifts , to estimate the binding affinity of the benzamidine-trypsin complex, achieving results that closely mirrored experimental findings. However, standard and biased MD methods require significant computational power that render these techniques unsuited for highthroughput screening purposes. Consequently, faster and less accurate methods such as molecular docking and machine learning (ML) approaches have been proposed as alternatives.