Receptor-based approaches and therapeutic targets in Alzheimer’s disease along with role of AI in drug designing: Unraveling pathologies and advancing treatment strategies

Abstract

Alzheimer’s disease (AD) is a prevalent cause of dementia in the elderly, characterized by progressive cognitive decline and neurodegeneration. This review focuses on the etiology of AD, the role of various receptors [TNF (Tumor necrosis factor) receptor, nAChR (Neuronal nicotinic acetylcholine receptors), NMDARs (N-Methyl-D-aspartate receptors), APOE (Apolipoprotein E) receptor, and amyloid-beta receptor], and risk factors contributing to its development. AD progresses through mild, moderate, and severe stages, each exhibiting distinct symptoms. The hallmark pathologies are neurofibrillary tangles and amyloid plaques, comprised of hyperphosphorylated tau protein and amyloid-beta peptides, respectively. Current pharmacotherapeutic options alleviate symptoms but lack a complete cure. To address the challenges in developing effective AD treatments, researchers have turned to artificial intelligence (AI) and computational approaches in drug design. AI techniques, including machine learning and molecular docking, enable the analysis of large datasets and prediction of molecular interactions between potential drug candidates and target receptors. Virtual screening and molecular modeling aid in identifying novel therapeutic compounds. Predictive modeling and optimization algorithms optimize drug properties and predict efficacy. AI also facilitates the repurposing of existing drugs by analyzing their interactions with AD-related receptors and pathways. Clinical trial optimization using AI algorithms enhances patient selection, treatment monitoring, and outcome prediction. Integrating AI into AD drug design holds tremendous promise for accelerating the discovery of effective interventions. By leveraging AI’s capabilities, researchers can efficiently analyze extensive data, predict drug-target interactions, and optimize drug properties, leading to the identification of novel AD treatments. However, further research and validation are needed to translate AI-driven drug design approaches into clinically viable solutions for AD patients. Through continued advancements in AI and collaborative efforts, the development of targeted and advanced therapies for AD is within reach.