Computational Approaches for Multitarget Drug Design in Alzheimer's Disease: A Comprehensive Review

Alzheimer's disease (AD) is a chronic and progressive neurodegenerative brain disorder, primarily affecting the elderly. Its socio-economic impact and mortality rate are alarming, necessitating innovative approaches to drug discovery. Unlike single-target diseases, Alzheimer's multifactorial nature makes single-target approaches less effective. To address this challenge, researchers are turning to drug design strategies targeting multiple disease pathways simultaneously. This approach has led to the promising identification of dual or multiple-target inhibitors, offering new perspectives for improving disease management. Computer-Aided Drug Design (CADD) such as virtual screening, docking, QSAR, molecular dynamics, ADMET prediction, etc., are valuable tools for designing and identifying new multi target directed ligands (MTDLs). These methods enable efficient screening of extensive compound libraries and accurate prediction of pharmacokinetic profiles, optimizing development costs and time. Challenges such as model accuracy, simulation complexity, and data integration persist. Addressing these issues requires advances in in silico modeling, high-performance computing, and experimental validation. In this regard, this review highlights recent advances using various computational methods to screen and identify new candidate compounds containing different heterocyclic motifs that could serve as potential bases for designing ligands targeting multiple targets for Alzheimer's disease.