da Conceicao_2025_J.Comput.Aided.Mol.Des_40_7

Alzheimer's disease (AD) is a progressive neurodegenerative disorder affecting millions of people worldwide, with its prevalence expected to rise in the coming years. Due to the complexity of AD and the intricate interplay among its pathological mechanisms, the development of multitarget-directed ligands (MTDLs) has emerged as a promising therapeutic strategy. These compounds could simultaneously modulate multiple pathogenic pathways. Specifically, cholinergic and amyloid mechanisms, implicated in the onset of the disease, are regulated by AChE and BACE1, respectively. Therefore, targeting both pathways offers substantial therapeutic potential for AD. Computational tools can be useful in the identification of potential MTDL for these enzymes, reducing both costs and time in the drug discovery process. This review explores the relevance of this approach in the research and development for novel AD therapies, highlighting ongoing efforts focused on the identification and development of MTDLs for AChE and BACE1 inhibition through in silico methods. Virtual screening was the most frequently applied technique for a fast selection of ligands based on their affinity for the enzymes of interest. The in silico ADMET prediction also appears with a technique that allows the screening of compounds with drug-likeness. Moreover, evidence suggests that combining multiple computational methods can effectively identify drug candidates with optimized properties for target modulation and brain bioavailability.