Currently, anti‐butyrylcholinesterase (anti‐BuChE)is among the greatest therapeutic agents for the treatment of Alzheimer's disease. In this research, a series of 36 carbamate derivatives were subjected to a quantitative structure–activity relationships study using DFT and Lipinski's descriptors. Multiple linear regression (MLR) was used to explore the relationships between the structural features of these compounds and BuChE inhibitory activity. In order to generate results applicable in the experimental plan, the Organization of Economic Cooperation and Development guidelines were adopted. The quality of MLR model was evaluated by several statistical parameters including internal and external validation parameters (R2, R2adj, F, VIF, R2test, Q2CV, R2Rand, Q2CV [Rand], and cRp2). The built model displayed a high predictive power (R2test = 0.817). What is more, the internal validation parameters (Q2CV = 0.774; average R2Rand = 0.118; average Q2CV [Rand] = −0.438; cRp2 = 0.820) highlight the robustness of our built model. Besides, the obtained result revealed that anti‐BuChE activity is mainly attributed to the following molecular descriptors: octanol–water partition (log P), highest occupied molecular orbital energy (EHOMO), total energy (ET), and dipole moment (μ). Based on these findings, a series of newer synthesizable compounds with enhanced anti‐BuChE activities were designed and their ADMET and drug‐likeness properties were further predicted to filter out compounds likely to fail during drug development stages. Finally, molecular docking and molecular dynamics were performed to identify the binding types between the best designed compounds and BuChE enzyme.

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