Prospective Current Novel Drug Target for the Identification of Natural Therapeutic Targets for Alzheimer's Disease

Abstract

In today's societies, Alzheimer's disease (AD) is a significant issue. In the US, more than five million people, most of whom are 65 or older, suffer from Alzheimer's disease. By 2060, there will be fourteen million Americans living with Alzheimer's disease, according to a report by the Alzheimer's Association. To find hits with polypharmacological activities, libraries of compounds can be biologically screened based on these targets. These hits can have their structural properties altered to improve the overall profile, just like molecules created using techniques based on knowledge or medicinal chemistry. Designing multi-target ligands against key targets of interest would undoubtedly benefit from knowledge of the roles played by various targets in the development of AD as well as pharmacophores with related biological activities. Computational tools are used to assist in the design of potential polypharmacological lead molecular scaffolds, in addition to knowledge-based and biological screening-based approaches. It is becoming more common to use pharmacophore modelling, machine learning, and structure-based virtual screening to forecast biological activity and target-ligand interaction for various chemical libraries.