Yasser Hayek-Orduz ¹ DORIAN ARMANDO ACEVEDO-CASTRO ¹ María Francisca Villegas-Torres ¹ Paola Andrea Caicedo ² Alvaro Barrera-Ocampo ² Natalie Cortes ³ Edison Humberto Osorio ³ Andres Fernando Gonzalez ^1*

• ¹ University of Los Andes, Colombia, Bogotá, Colombia
• ² ICESI University, Cali, Cauca, Colombia
• ³ University of Ibagué, Ibagué, Tolima, Colombia

Therapeutic strategies for Alzheimer's disease (AD) often involve inhibiting acetylcholinesterase (AChE), underscoring the need for novel inhibitors with greater selectivity for AChE to minimize side effects. This can be facilitated by a detailed analysis of the protein-ligand pharmacophore dynamics. In this study, we developed and employed dypHAI, an innovative approach integrating machine learning models, ligand-based pharmacophore models, and complex-based pharmacophore models into a pharmacophore model ensemble. This ensemble captures key protein-ligand interactions, including π-cation interactions with Trp-86 and several π-π interactions with residues Tyr-341, Tyr-337, Tyr-124, and Tyr-72. The protocol identified 18 novel molecules from the ZINC database with binding energy values ranging from -62 to -115 kJ/mol, suggesting their strong potential as AChE inhibitors. To further validate the predictions, nine molecules were synthesized and tested for their inhibitory activity against human AChE. Experimental results revealed that molecules 4 (P-1894047), with its complex multi-ring structure and numerous hydrogen bond acceptors, and 7 (P-2652815), characterized by a flexible, polar framework with ten hydrogen bond donors and acceptors, exhibited IC₅₀ values lower than or equal to that of the control (galantamine), indicating potent inhibitory activity. Similarly, molecules 5 (P-1205609), 6 (P-1206762), 8 (P-2026435), and 9 (P-533735) also demonstrated strong inhibition. In contrast, molecule 3 (P-617769798) showed a higher IC₅₀ value, and molecules 1 (P-14421887) and 2 (P-25746649) yielded inconsistent results, likely due to solubility issues in the experimental setup. These findings underscore the value of integrating computational predictions with experimental validation, enhancing the reliability of virtual screening in the discovery of potent enzyme inhibitors.