Abstract
Acetylcholinesterase (AChE) has proven to be an effective drug target in the treatment of neurodegenerative diseases such as Alzheimer's, Parkinson's and dementia. We developed a novel QSAR regression model for estimating potency to inhibit AChE, pK (i), on a set of 75 structurally different compounds including oximes, N-hydroxyiminoacetamides, 4-aminoquinolines and flavonoids. Although the model included only three simple descriptors, the valence molecular connectivity index of the zero-order, (0) χ(v) , the number of 10-membered rings (nR10) and the number of hydroxyl groups (nOH), it yielded excellent statistics (r = 0.937, S.E. = 0.51). The stability of the model was evaluated when an initial set of 75 compounds was broadened to 165 compounds in total, with the increase of the range of pK (i) (exp) from 6.0 to 10.2, yielding r = 0.882 and S.E. = 0.89. The predictive power of the model was evaluated by calculating pK (i) values for 55 randomly chosen compounds (S.E.(test) = 0.90) from the calibration model created on other 110 compounds (S.E. = 0.89), all taken from the pool of 165 compounds.