Development of Activity Rules and Chemical Fragment Design for In Silico Discovery of AChE and BACE1 Dual Inhibitors against Alzheimer's Disease

Machine learning in Alzheimer's disease drug discovery and target identification

Alzheimer's disease (AD) stands as a formidable neurodegenerative ailment that poses a substantial threat to the elderly population, with no known curative or disease-slowing drugs in existence. Among the vital and time-consuming stages in the drug discovery process, disease modeling and target identification hold particular significance. Disease modeling allows for a deeper comprehension of disease progression mechanisms and potential therapeutic avenues. On the other hand, target identification serves as the foundational step in drug development, exerting a profound influence on all subsequent phases and ultimately determining the success rate of drug development endeavors. Machine learning (ML) techniques have ushered in transformative breakthroughs in the realm of target discovery. Leveraging the strengths of large dataset analysis, multifaceted data processing, and the exploration of intricate biological mechanisms, ML has become instrumental in the quest for effective AD treatments. In this comprehensive review, we offer an account of how ML methodologies are being deployed in the pursuit of drug discovery for AD. Furthermore, we provide an overview of the utilization of ML in uncovering potential intervention strategies and prospective therapeutic targets for AD. Finally, we discuss the principal challenges and limitations currently faced by these approaches. We also explore the avenues for future research that hold promise in addressing these challenges.

Recent Development of Novel Aminoethyl-Substituted Chalcones as Potential Drug Candidates for the Treatment of Alzheimer's Disease

No drug on the market, as a single entity, participates in different pathways involved in the pathology of Alzheimer's disease. The current study is aimed at the exploration of multifunctional chalcone derivatives which can act on multiple targets involved in Alzheimer's disease. A series of novel aminoethyl-substituted chalcones have been developed using in silico approaches (scaffold morphing, molecular docking, and ADME) and reported synthetic methods. The synthesized analogs were characterized and evaluated biologically using different in vitro assays against AChE, AGEs, and radical formation. Among all compounds, compound PS-10 was found to have potent AChE inhibitory activity (IC50 = 15.3 nM), even more than the standard drug (IC50 = 15.68 nM). Further, the in vivo evaluation of PS-10 against STZ-induced dementia in rats showed memory improvement (Morris Water Maze test) in rats. Also, PS-10 inhibited STZ-induced brain AChE activity and oxidative stress, further strengthening the observed in vitro effects. Further, the molecular dynamic simulation studies displayed the stability of the PS-10 and AChE complex. The novel aminoethyl-substituted chalcones might be considered potential multifunctional anti-Alzheimer's molecules.

Identification Mechanism of BACE1 on Inhibitors Probed by Using Multiple Separate Molecular Dynamics Simulations and Comparative Calculations of Binding Free Energies

β-amyloid cleaving enzyme 1 (BACE1) is regarded as an important target of drug design toward the treatment of Alzheimer's disease (AD). In this study, three separate molecular dynamics (MD) simulations and calculations of binding free energies were carried out to comparatively determine the identification mechanism of BACE1 for three inhibitors, 60W, 954 and 60X. The analyses of MD trajectories indicated that the presence of three inhibitors influences the structural stability, flexibility and internal dynamics of BACE1. Binding free energies calculated by using solvated interaction energy (SIE) and molecular mechanics generalized Born surface area (MM-GBSA) methods reveal that the hydrophobic interactions provide decisive forces for inhibitor-BACE1 binding. The calculations of residue-based free energy decomposition suggest that the sidechains of residues L91, D93, S96, V130, Q134, W137, F169 and I179 play key roles in inhibitor-BACE1 binding, which provides a direction for future drug design toward the treatment of AD.