Current Topics in Medicinal Chemistry - Volume 25, Issue 4, 2025

Alzheimer's Disease (AD) is a serious neurological illness that causes memory loss gradually by destroying brain cells. This deadly brain illness primarily strikes the elderly, impairing their cognitive and bodily abilities until brain shrinkage occurs. Modern techniques are required for an accurate diagnosis of AD. Machine learning has gained attraction in the medical field as a means of determining a person's risk of developing AD in its early stages. One of the most advanced soft computing neural network-based Deep Learning (DL) methodologies has garnered significant interest among researchers in automating early-stage AD diagnosis. Hence, a comprehensive review is necessary to gain insights into DL techniques for the advancement of more effective methods for diagnosing AD.

This review explores multiple biomarkers associated with Alzheimer's Disease (AD) and various DL methodologies, including Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), The k-nearest-neighbor (k-NN), Deep Boltzmann Machines (DBM), and Deep Belief Networks (DBN), which have been employed for automating the early diagnosis of AD. Moreover, the unique contributions of this review include the classification of ATN biomarkers for Alzheimer's Disease (AD), systemic description of diverse DL algorithms for early AD assessment, along with a discussion of widely utilized online datasets such as ADNI, OASIS, etc. Additionally, this review provides perspectives on future trends derived from critical evaluation of each variant of DL techniques across different modalities, dataset sources, AUC values, and accuracies.

The search for effective cancer therapies highlights saponins, natural plant-derived compounds, as promising anticancer agents. These compounds induce apoptosis in cancer cells by activating caspases, essential enzymes for cell death. For example, Soyasapogenol B from Glycine max and Astragaloside IV from Astragalus membranaceus effectively trigger apoptosis in cancer cells. Additionally, saponins, such as Compound K from American ginseng and Saikosaponin from Bupleurum falcatum, affect extrinsic and intrinsic pathways, including mitochondrial release of cytochrome C and activation of caspase-9. Ziyuglycoside II also acts on both pathways and activates the ROS/JNK pathway. Understanding these mechanisms provides promising prospects for developing more specific and safer anticancer therapies. The review utilized the ScienceDirect, PubMed, and Google Scholar databases. It was found that original articles and reviews from journals indexed in these sources emphasized the antitumor capabilities of saponins and discussed their role in apoptosis induction and caspase activation. The activation of caspases by saponins in the apoptotic pathway involves two main pathways: the extrinsic pathway is initiated by external signals that activate caspase-8, while the intrinsic pathway starts with internal stimuli, causing the release of cytochrome c and the activation of caspase-9. These pathways both lead to the activation of effector caspases (caspases 3, 6, and 7), culminating in apoptosis, an essential process for maintaining cellular balance and eliminating damaged cells. Identifying saponins in the context of cancer and their mechanisms of action is an ever-evolving field. Future research may lead to more targeted and personalized therapies, highlighting the collaboration between basic and clinical research in this promising area of medicine.

Rapid growth in nanotechnology, also known as 21^st-century technology, is occurring in response to the increasing diversity of diseases. The development of safe and effective drug delivery methods to enhance bioavailability is of paramount importance. Researchers have focused on creating safe, cost-effective, and environmentally friendly nanoparticle construction processes. Natural polysaccharides, a type of multifaceted polymer with a wide range of applications and advantages, are particularly well suited for nanoparticle formulations, as they can mitigate the adverse consequences of synthetic nanoparticle formulations and promote sustainability. This review summarizes various sources of natural-based polysaccharides and polyacrylamide-based nanomaterials in nanoparticle preparation. Additionally, it discusses the use of natural polysaccharides in formulations beyond nanotechnology, highlighting their importance in green synthesis and different preparation methods.

The c-Met receptor, a pivotal player in oncogenesis and tumor progression, has become a compelling target for anticancer drug development. This review explores the intricate landscape of Structure-Activity Relationship (SAR) studies and molecular binding analyses performed on c-Met inhibitors. Through a comprehensive examination of various chemical scaffolds and modifications, SAR investigations have elucidated critical molecular features essential for the potent inhibition of c-Met activity. Additionally, molecular docking studies have provided invaluable insights into how c-Met inhibitors interact with their target receptor, facilitating the rational design of novel compounds with enhanced efficacy and selectivity. This review highlights key findings from recent SAR and docking studies, particularly focusing on the structural determinants that govern inhibition potency and selectivity. Furthermore, the integration of computational methodologies with experimental approaches has accelerated the discovery and optimization of c-Met inhibitors, fostering the advancement of promising candidates for clinical applications. Overall, this review underscores the pivotal role of SAR and molecular docking studies in advancing our understanding of c-Met inhibition and guiding the rational design of next-generation anticancer agents targeting this pathway.