Role of multi-targeted bioactive natural molecules and their derivatives in the treatment of Alzheimer's disease: an insight into structure-activity relationship.

Alzheimer's disease (AD) is a complex neurodegenerative disorder involving cognitive dysfunction like short-term memory and behavioral changes as the disease progresses due to other unaltered physiological factors. The solution for this problem is Multi-targeted Drugs (MTDs), which can affect multiple determinants to realize the multifunctional effects. Acetylcholinesterase (AChE) inhibitors donepezil, rivastigmine, galantamine, and N-methyl-D-aspartate (NMDA) receptor antagonist memantine are FDA-approved drugs used to treat AD symptomatically. The key objective of this review is to understand multitargeted bioactive natural molecules that could be considered as leads for further development as effective drugs for treating AD, along with understanding its pharmacology and structure-activity relationship (SAR). Understanding the molecular mechanism of the AD pathophysiology, the role of existing drugs, treatment of AD via amyloid beta (Aß) plaque, and neurofibrillary tangle (NFT) inhibition by natural bioactive molecules were also discussed in the review. The current quest and recent advancements with natural bioactive compounds like physostigmine, resveratrol, curcumin, and catechins, along with the study of in silico SAR, were reported in the present study. This review summarises the structural properties required for bioactive natural molecules to show anti-Alzheimer's activity by emphasizing on SAR of several bioactive natural molecules targeting various AD pathologies, their key molecular interactions that are critical for target specificity, their role as multitargeted ligands, used with adjunctive therapy for AD followed by related US patents granted recently. This article highlights the significance of the structural features of natural bioactive molecules in the treatment of AD and establishes a connection between them.Communicated by Ramaswamy H. Sarma.

Molecular docking and dynamics based approach for the identification of kinase inhibitors targeting PI3Kα against non-small cell lung cancer: a computational study.

Non-small cell lung cancer (NSCLC) is an obscure disease whose incidence is increasing worldwide day by day, and PI3Kα is one of the major targets for cell proliferation due to the mutation. Since PI3K is a class of kinase enzyme, and no in silico research has been performed on the inhibition of PI3Kα mutation by small molecules, we have selected the protein kinase inhibitor database and performed the energy minimization process by ligand preparation. The key objective of this research is to identify the potential hits from the protein kinase inhibitor library and further to perform lead optimization by a molecular docking and dynamics approach. And so, the protein was selected (PDB ID: 4JPS), having a unique inhibitor and a specific binding pocket with amino acid residue for the inhibition of kinase activity. After the docking protocol validation, structure-based virtual screening by molecular docking and MMGBSA binding affinity calculations were performed and a total of ten hits were reported. Detailed analysis of the best scoring molecules was performed with ADMET analysis, induced fit docking (IFD) and molecular dynamics (MD) simulation. Two molecules - 6943 and 34100 - were considered lead molecules and showed better results than the PI3K inhibitor Copanlisib in the docking assessment, ADMET analysis, and molecular dynamics simulation. Furthermore, the synthetic accessibility of the two compounds - 6943 and 34100 - was investigated using SwissADME, and the two lead molecules are easier to synthesize than the PI3K inhibitor Copanlisib. Computational drug discovery tools were used for identification of kinase inhibitors as anti-cancer agents for NSCLC in the present research.