Searching for AChE inhibitors from natural compounds by using machine learning and atomistic simulations

Characterizing the ligand-binding affinity toward SARS-CoV-2 Mproviaphysics- and knowledge-based approaches

“…In this context, the computational cost would be reduced when the pulling velocity is high and the cantilever spring constant is large. 76,77 The maximum pulling force F Max , known as the rupture force, can be used as a metric to rank the ligand-binding affinity with the assumption that a stronger binding ligand requires a larger pulling force. 25 In addition, the pulling work W was proven to be a better quantity to predict the relative binding affinities among various inhibitors since it is associated with the Δ G via Jarzynski equality.…”

Identifying Possible AChE Inhibitors from Drug-like Molecules via Machine Learning and Experimental Studies

“…Moreover, the Pearson’s R over these complexes was 0.863 ± 0.091 (see Figure ). It is significantly larger than that by the default parameter, which is of R = 0.75 ± 11 . Therefore, it may be concluded that AutoDock Vina with the modified empirical parameters is an appropriate approach to preliminarily predict the ligand binding pose and affinity of ligands to AChE.…”

“…It is significantly larger than that by the default parameter, which is of R = 0.75 ± 11. 89 Therefore, it may be concluded that AutoDock Vina with the modified empirical parameters is an appropriate approach to preliminarily predict the ligand binding pose and affinity of ligands to AChE. The approach was thus utilized to estimate the binding poses of eight compounds, which were indicated by ML calculation and confirmed via the in vitro enzyme assay.…”