Evaluating computational and experimental approaches in early-stage Alzheimer's drug discovery: a systematic review

Alzheimer's disease (AD) represents a significant global health challenge due to its complex pathophysiology and limited therapeutic options. Traditional drug discovery methods have had limited success, highlighting the need for innovative strategies. This systematic review evaluates the role of molecular docking, virtual screening, and molecular dynamics simulations in the early stages of AD drug discovery. This study reviewed 100 studies published between 2000 and 2024, focusing on computational approaches to identify and optimize drug candidates targeting key AD-related proteins, including acetylcholinesterase (AChE), β-secretase (BACE1), and tau. Both natural and synthetic compounds were examined, emphasizing studies integrating in silico methods with in vitro and in vivo validations. AChE was the most frequently targeted protein (23 studies), followed by BACE1 and multi-target approaches. The compounds investigated varied, with 35 studies focusing on natural products (e.g., quercetin, huperzine A) and 54 on synthetic analogs (e.g., tacrine derivatives). Integrating computational and experimental methods enhanced the validation process, providing comprehensive insights into the pharmacodynamics and pharmacokinetics of potential therapeutics. Computational approaches significantly expedite the identification and optimization of AD drug candidates by enabling the rapid screening of extensive compound libraries. These methods, when combined with experimental validations, offer deeper molecular-level insights into drug interactions and mechanisms. However, challenges such as predictive accuracy and data quality remain, necessitating further advancements in computational models and data integration to improve the predictability and effectiveness of AD therapeutics.