Implementation of Computational and Machine learning techniques for the development of in silico tools and identification of novel leads for the treatment of Alzheimer's Disease

Abstract

Alzheimer's disease (AD) is the most common form of dementia causing memory, behaviour and thinking impairment. Eventually, the symptoms become severe and make it difficult for a patient to carry out daily activities. According to the World Health Organization (WHO), one in every 85 individuals will have AD by 2050. The therapeutic targets of the disease include acetylcholinesterase (AChE), butyrylcholinesterase (BChE), β-secretase-1, glycogen synthase kinase 3β, monoamine oxidase B, matrix metalloproteases, N-methyl D-aspartate (NMDA) receptors, tau kinase etc.