The reversible inhibition of acetylcholinesterase (AChE) has become a promising target for the treatment of Alzheimer's disease (AD) which is mainly associated with low in vivo levels of acetylcholine (ACh). The availability of AChE crystal structures with and without a ligand triggered the effort to find a structure-based design of acetylcholinesterase inhibitors (AChEIs) for AD. The major problem observed with the structure-based design was the feeble robustness of the scoring functions toward the correlation of docking scores with inhibitory potencies of known ligands. This prompted us to develop new prediction models using the stepwise regression analysis based on consensus of different docking and their scoring methods (GOLD, LigandFit, and GLIDE). In the present investigation, a dataset of 91 molecules belonging to 9 different structural classes of heterocyclic compounds with an activity range of 0.008 to 281,000 nM was considered for docking studies and development of AChE-specific 3D-QSAR models. The model (M1) developed using consensus of docking scores of scoring functions viz. Glide score, Gold score, Chem score, ASP score, PMF score, and DOCK score was found to be the best (R(2) = 0.938, Q(2) = 0.925, R(pred)(2) = 0.919, R(2)m((overall)) = 0.936) compared to other consensus models. Docking studies revealed that the molecules with proper alignment in the active site gorge and the ability to interact with all the crucial amino acid residues, in particular by forming π-π stacking interactions with Trp84 at the catalytic anionic site (CAS) and Trp279 at peripheral anionic site (PAS), showed augmented potencies with consequent improvement in patient cognition and reduced the formation of senile plaques associated with AD. Further, the descriptors that signify the association of the ligands with the receptor as well as ADME properties of the ligands were also analyzed by means of the set of ligands that have been pre-positioned with respect to a receptor after docking analysis and considered as independent variables to generate a linear model (M3 and M4) using a stepwise multiple linear regression method to get additional insight into the physicochemical requirements for effective binding of ligands with AChE as well as for prediction of AChE inhibition. The developed AChE-specific prediction models (M1-M4) satisfactorily reflect the structure-activity relationship of the existing AChEIs and have all the potential to facilitate the process of design and development of new potent AChEIs.