Abstract / Summary
Integrating computer-aided drug design (CADD) through Quantitative Structure-Activity Relationship (QSAR) modeling, multi-target molecular docking, ADMET profiling, and molecular dynamics (MD) simulations offers an efficient strategy for identifying promising benzylpiperidine-diarylthiazole hybrids against key Alzheimer’s disease (AD) targets. In this study, a four-descriptor QSAR model was established using Genetic Algorithm-Multiple Linear Regression (GA-MLR) to evaluate structural determinants of anti-cholinesterase activity. Based on multi-target molecular docking against FGFR3, BACE1, and D2 dopamine receptors, a parent template (Ligand 13) was selected to design novel analogs. The top predicted leads (L3, L4, and L6), the reference drug Donepezil, and the Ligand 13 were subjected to 200 ns MD simulations against FGFR3, supplemented with linear interaction energy (LIE), MM/PBSA, and MM/GBSA thermodynamic endpoint protocols. The QSAR model demonstrated statistical robustness (R 2 = 0.8113, Q 2 loo = 0.7664) within its defined applicability domain. While ligand 13 presented lipophilicity liabilities (MLogP = 5.75), the designed analogs L3, L4, and L6 exhibited improved predicted drug-likeness, high human intestinal absorption (85.46% – 92.05%), and favourable blood-brain barrier permeability predictions (logBB up to 0.624 for L6). MD trajectory analysis for FGFR3 showed that L4 maintained low conformational fluctuation (RMSD = 1.548 Å). End-state free energy computations for FGFR3 indicated a favourable binding energy profile for L6 (MM/PBSA = −14.6419 ± 2.9670 kcal/mol; MM/GBSA = −32.9258 ± 0.0704 kcal/mol), displaying more negative binding free-energy estimates than Donepezil in this simulation setup. These computational findings position L4 and L6 as promising structural leads for further experimental in vitro and in vivo validation.