Computer-Aided Multi-Target Management of Emergent Alzheimer’s Disease

Abstract

Alzheimer's disease (AD) represents an enormous global health burden in terms of human suffering and economic cost. AD management requires a shift from the prevailing paradigm targeting pathogenesis to design and develop effective drugs with adequate success in clinical trials. Therefore, it is of interest to report a review on amyloid beta (Aβ) effects and other multi-targets including cholinesterase, NFTs, tau protein and TNF associated with brain cell death to be neuro-protective from AD. It should be noted that these molecules have been generated either by target-based or phenotypic methods. Hence, the use of recent advancements in nanomedicine and other natural compounds screening tools as a feasible alternative for circumventing specific liabilities is realized. We review recent developments in the design and identification of neuro-degenerative compounds against AD generated using current advancements in computational multi-target modeling algorithms reflected by theragnosis (combination of diagnostic tests and therapy) concern.

Computer-Aided Drug Design of β-Secretase, γ-Secretase and Anti-Tau Inhibitors for the Discovery of Novel Alzheimer’s Therapeutics

“…Enhanced sampling MD and conformational analysis optimized for disordered targets, coupled with computational docking and machine learning were employed to successfully identify novel chemically diverse Tau ligands including an inhibitor that delays the in vitro aggregation reaction without affecting the amount of aggregate formed at the steady state [166]. Other CADD studies for the development of anti-Tau inhibitors are summarized in previously published review articles [167,168].…”