Nowadays, health issues are common due to lifestyle modifications and a lack of body immunity. This study primarily focuses on the putative action of Paerida foetida by using a hydroalcoholic extract for antioxidant and liver disorders. Antioxidant properties were evaluated by using DPPH radical scavenging assays. An LC-MS study was carried out for the characterization of compounds. Total Phenolic and flavonoid content were estimated using standard methods. Hepatotoxicity was induced in Swiss mice (25-35 g) by a CCl₄ and corn oil (1:1 v/v) mixture at 2 ml/kg. Then they are treated with Paerida foetida (500 gm/kg and 250 mg/kg) hydroalcoholic extract orally for 21 days. Paerida foetida (500mg/kg) extract exhibits a significant preventive action against liver disorders, as evidenced by a significant reduction (p < 0.001) in high serum levels of AST, ALT, ALP, total bilirubin, and total protein content. The phenolic and flavonoid contents in P. foetida are responsible for antioxidant and hepatoprotective activities. The underlying molecule of Paerida foetida is responsible for an effective ameliorator of liver disease changes at the hepatic level (p < 0.001) of MDA, GSH, and SOD. Furthermore, hepatological studies of CCl4-induced necrosis cells have shown an effective cell-healing potency by sylimarin and P. foetida (500mg/kg) extract. LC-MS data revealed the presence of Pterostilbene, flavone, Neoagarobiose, Lathosterol, Sitosterol, and many other acidic esters. Finally, it is concluded that the hydroalcoholic extract of Paerida foetida is a valuable therapeutic herb to prevent liver deterioration and also an antioxidant agent due to the presence of the above-mentioned constituents as major compounds, identified by LC-MS in the plant extract.

Abstract: The drug discovery landscape and neurodegenerative disease diagnostics are expe-riencing a paradigm shift with the integration of artificial intelligence (AI) and machine learn-ing (ML) technologies. This review provides an elaborate discussion of AI-based approaches across all stages of computer-aided drug design (CADD), including virtual screening, peptide synthesis, pharmacophore modelling, quantitative structure–activity relationships (QSAR), and drug repurposing. Particular emphasis is placed on Alzheimer's disease (AD), a multifac-eted neurodegenerative disorder, with AI enabling early diagnosis, patient stratification, and subtype classification to support precision medicine. The review highlights recent developments in supervised and unsupervised learning, omics integration, and the increasing importance of explainable AI (XAI) in addressing the “black box” limitations of traditional AI models. We categorize and assess major AI platforms and tools by their scope, stage of implementation, and form of explainability, offering a practical framework for their application in pharmaceutical and clinical research. The combined use of deep learning, quantum simulation, and virtual reality is also discussed in the context of de novo drug design, compound screening, and toxicity prediction. With con-tinued advances in AI, interdisciplinary collaboration and ethical oversight are essential to translate these digital innovations into effective, safe, and personalized therapies. This review serves as an all-encompassing guide for researchers, clinicians, and developers working at the interface of AI and drug development, particularly those addressing the unmet challenges in Alzheimer's disease. As outlined in the abstract and reflected in the article title, this review bridges AI-driven drug discovery and Alzheimer’s diagnosis, with specific empha-sis on explainable models for next-generation therapeutics. The subsequent sections expand on these themes, offering a structured synthesis of tools, applications, and methodological frameworks.

Abstract: Interest in artificial intelligence (AI) in the medical and pharmaceutical sciences has increased dramatically in recent years. AI encompasses techniques such as supervised, unsupervised, and reinforcement learning, which allow for rapid analysis of big datasets and reliable predictive modelling. This review examines the history and development of AI, distinguishes between human and artificial intelligence, and evaluates current and emerging applications in pharmacy—including community pharmacy, hospital pharmacy, predictive toxicology, personalized medicine and ge-nomics, clinical pharmacy, radiopharmaceuticals, drug manufacturing, and research pharmacy, i.e. in silico drug design and discovery, lead screening, target identification, and lead optimization. Furthermore, implications of explainable AI and associated ethical considerations will be thoroughly discussed.

Prostate cancer is one of the most prevalent death-causing diseases among males, and metastatic progression leads to significant mortality. The transmembrane serine protease 2 (TMPRSS2) is often overexpressed or fused with the ETS-related gene (ERG) in prostate cancer patients. These molecular alterations play a pivotal role in tumour progression and metastasis. This integrated computational drug repurposing approach aimed to identify and characterize potential TMPRSS2 inhibitors among Food and Drug Administration (FDA) approved compounds. Given the exclusively in silico nature of this work, the findings should be interpreted as preliminary. A high-throughput molecular docking screening was conducted using FDA-approved compounds from DrugBank against the TMPRSS2 catalytic domain (active site). Top-scoring ligands were further analysed through pharmacokinetic profiling. Likewise, 1 μs molecular dynamics simulations (MDS) were performed within a lipid bilayer environment. Sta-bility and interaction strength were evaluated through root mean square deviation (RMSD), hydrogen bonding, and radius of gyration (rGyr). Moreover, solvent-accessible surface area (SASA), polar surface area (PSA), cluster analysis, and principal component analysis (PCA) were also used for evaluation. Through high binding affinities and stable interactions of drugs, Iotrolan, Iodixanol, and Hyaluronate emerged as top candidates. The Iotrolan-TMPRSS2 complex demonstrated the greatest stability, with an average protein RMSD of 2.44 Å. Likewise 6,988 hydrogen bonds, and 9,100 water-bridge formations contributed to the stability of Iotrolan-TMPRSS2 complex. Structural metrics (rGyr, SASA, PSA) indicated compact and stable ligand conformations throughout the simulation. Clustering analysis showed that over 60% of simulation frames for Iotrolan localized within five dominant conformational clusters. PCA revealed that the first three eigenvectors accounted for 63% of the total motion, suggesting ligand-induced modulation of TMPRSS2 dynamics. Iotrolan stabilizes the protein structure and restricts its conformational flexibility exhibited strong and persistent interactions with TMPRSS2, which suggests a stronger inhibitory potential compared to in silico. Iodixanol showed moderate stabilization with fewer persistent contacts. Hyaluronate displayed internal ligand stability but induced higher protein flexibility, potentially reducing inhibitory efficacy. Despite promising interactions, poor oral bioavailability and membrane permeability highlight the need for optimization and further experimental evaluation. This multi-step combinational computational analysis identifies Iotrolan as a pre-liminary and promising candidate for further investigation as a TMPRSS2 inhibitor. These findings represent preliminary computational predictions only and should be interpreted with caution. They require rigorous in vitro and in vivo validation before any biological or therapeutic relevance can be established. This work provides a structural framework that may guide future experimental studies in TMPRSS2-targeted drug discovery.

The main goal is to understand how quality by design, process analytical technologies, and green chemistry are used to improve the drug discovery process. These innovative approaches improve product quality, reduce the environmental effects of pharmaceutical production, and increase the efficiency of drug development for better patient compliance. Process analytical technology, experimental design, and risk assessment techniques are used to obtain a deeper understanding of materials and processes. There is no denying that the use of process analytical technologies can greatly facilitate the application of QbD for dependable and sustainable product development. Through the integration of risk-based techniques and process optimization, QbD has resulted in reduced variability and more consistent product quality. The systematic drug discovery process has been greatly improved by the use of QbD, PAT tools, and green chemistry. PAT tools have made manufacturing more robust and efficient by enabling real-time monitoring, enhancing process control, and lowering production errors. By using sustainable raw materials, reducing waste, and optimizing energy use, green chemistry has decreased environmental impact while also lowering production costs. Drug discovery is being revolutionized by the combination of QbD, PAT tools, and green analytical chemistry, which maximizes efficiency, improves product quality, and fosters sustainability. These developments pave the way for more dependable and environmentally sustainable pharmaceutical manufacturing by simplifying the development process, ensuring more consistent results, and reducing environmental impacts.

The effective integration of blockchain technology and artificial intelligence (AI) has the potential to change healthcare management. Two cutting-edge developments in the healthcare industry are blockchain and artificial intelligence. Blockchain, an open network for information sharing and permission, is being used in e-Health to apply artificial intelligence models. Healthcare workers will be able to view patient medical records on the blockchain. Artificial intelligence (AI) makes use of a wide range of suggested algorithms, decision-making power, and vast amounts of data. Health care services can be made more decentralized, transparent, safe, and impenetrable with the use of blockchain technology. AI needs cryptographic records to be stored, and blockchain makes this possible. Applications of artificial intelligence in healthcare management include chatbots for diagnosis and treatment, predictive analytics, personalized medicine, and more. Blockchain technology has applications in supply chain management, clinical trial management, interoperability, and data security and integrity in healthcare management. A more effective, safe, and patient-centered healthcare ecosystem can be created by integrating blockchain technology and artificial intelligence (AI) into healthcare management. It can encourage the development of a wide range of applications in radiology, cancer treatment, cardiology, dermatology, and fundoscopy that may save patients' lives.

Cannabigerol (CBG), being one of the non-psychotropic phyto-cannabinoid, has been labelled and recognized to be antioxidant and neuroprotective; it may conceivably hold depression-relieving activity. Consequently, the objective of the present research procedure was to explore the depression-alleviating competence of cannabigerol in both stressed and unstressed mice using computational/in-silico modelling, followed by in-vivo analysis. Target genes for Major Depressive Disorder (MDD) were identified using GeneCards and Swiss Target Prediction, with common targets screened via Venny software. STRING database analysis established protein-protein interactions (PPI), identifying CNR2 (CB2 receptor) as a key target. Molecular docking of CBG with CB2 (PDB ID: 8GUR) showed strong binding, prompting in-vivo evaluation. ADME profiling via Schrödinger Maestro v10.5 confirmed CBG's high oral absorption and favorable pharmacokinetics. Male Swiss albino mice underwent chronic unpredictable mild stress (CUMS) for three successive weeks, with CBG (10, 20, 40 mg/kg) and imipramine (15 mg/kg) administered and various behavioral and biochemical parameters being analyzed. Cannabigerol demonstrated maximum oral absorption in ADME predictions using Schrödinger's Maestro (v10.5). Wayne diagram illustrated MDD-related targets, with CB2 (CNR2) rankings in top targets, based on SwissADME and Venny software analysis. Docking analysis revealed a high binding affinity (-10.53) for CB2, outperforming cannabidiol (-9.56) and comparable to Δ9-THC (-10.11). During in-vivo evaluation, CBG (40 mg/kg) and Imipramine 15 mg/kg significantly reduced CUMS-induced exalted plasma corticosterone, nitrite quantities, and monoamine oxidase-A action in the brain of stressed mice. Additionally, both treatments substantially reversed the unpredictable chronic stress-induced decline in catalase action, demonstrating CBG's possible potential in alleviating depression-like symptoms in mice. Cannabigerol has shown significant depressive alleviating potential in mice exposed to chronic and unpredictable stress regimes, possibly via interaction with cannabinoid receptors as indicated by in-silico modelling, which has been validated by our findings of the in-vivo protocol.

Vitamin D plays a crucial role in maintaining muscle and bone health and has been increasingly implicated in neurological disorders, including depression and anxiety, which are conditions closely associated with dysregulation of the serotonin 1A receptor (5-HT1A receptor). This study employs molecular modeling techniques to investigate the potential agonistic activity of Vitamin D on the 5-HT1A receptor. Additionally, it seeks to elucidate the key structural motifs and molecular interactions that underline the binding affinity between Vitamin D and the receptor. The insights gained from this research may inform the design of Vitamin D-derived compounds with optimized pharmacological profiles, contributing to therapeutic advancements in related neurological conditions. We selected five structures of the 5-HT1A receptor (PDB IDs: 7E2Y, 7E2Z, 8W8B, 8JSP, and 8JT6) for Protein-Ligand Interaction Fingerprint (PLIF) analysis. We conducted molecular docking to evaluate the binding efficiency of two forms of Vitamin D, ergocalciferol and cholecalciferol, to the 5-HT1A receptor. Following this, we performed Molecular Dynamics (MD) simulations to assess the stability of these interactions. Docking results revealed binding energies below -6.64 kcal/mol for both forms of Vitamin D, with ergocalciferol achieving a maximum binding energy of -7.78 kcal/mol. ASP116 emerged as a pivotal residue in stabilizing these interactions. MD simulations indicated that the Vitamin D-5-HT1A complexes exhibited stability comparable to the serotonin-bound 5-HT1A receptor complex. Our study suggests that Vitamin D may function as an agonist for the 5-HT1A receptor, with ASP116 playing a critical role in binding. Yet, further in vitro and in vivo studies are necessary to validate these findings and explore the therapeutic potential of Vitamin D-derived compounds.

Benzodiazepine (BZD) and thienodiazepine (TND) congeners are highly effective in managing central nervous system (CNS) disorders such as anxiety, insomnia, depression, and epilepsy. However, this class of compounds should be revitalized through the development of new molecules with minimal side effects and reduced potential for dependence. The BZDs and TNDs can occupy the BZD receptor situated at the allosteric site of gamma-aminobutyric acid type A (GABAA) receptor and facilitate the GABAA-mediated chloride ion channel opening action. The present study aims to structure-based docking of 29 BZDs and TNDs with binding affinity to enhance the allosteric action on GABAA and to predict the biochemical mechanisms at the target. This aspect has been scarcely explored; therefore, our objective is to predict the key amino acids within the target that favor interactions with BZDs and TNDs using quantitative structure-activity-amino acid relationship (QSAAR) analysis. The developed docking and QSAAR models can explain how interacting amino acids affect biological activity in terms of GABAA receptor binding affinity of BZDs and TNDs. The QSAAR establishes a quantitative relationship between biological activity and critical amino acids interacting with various groups of chemical compounds. The above QSAAR model identifies GLN1239, SER1240, THR1242, and VAL1247 as significant contributors to activity with an R-value of 0.77. Therefore, these interacting amino acids are responsible for the compounds' agonistic activity.

Ethnopharmacology is the study of traditional medicinal knowledge and its application in modern drug discovery. It combines ethnobotanical insights with scientific research to identify bio-active compounds with therapeutic potential. Sustainable agriculture refers to farming practices that maintain ecological balance, support biodiversity, and ensure the long-term availability of resources. Integrating these fields can enhance drug discovery while preserving medicinal plants and promoting environmental sustainability. This review examines the collaboration between ethnopharmacology and sustainable agriculture in advancing drug discovery, conservation, and global food security. This review examines the role of ethnopharmacology in drug discovery, analyzing traditional medicinal practices, bioactivity-guided fractionation, and metabolomic profiling. It also investigates sustainable agriculture techniques, including organic farming, controlled cultivation, and conservation strategies for medicinal plants. Data were collected from peer-reviewed literature using sources such as Google Scholar, PubMed, Scopus, and journal databases like ScienceDirect. Ethnopharmacology has con-tributed to the discovery of key drugs with anticancer, anti-inflammatory, and antimicrobial proper-ties. Sustainable agriculture ensures a steady supply of medicinal plants while optimizing their bio-active compound production through improved cultivation techniques. The combination of these ap-proaches strengthens drug discovery efforts and supports ecological conservation. Integrating eth-nopharmacology with sustainable agriculture is a promising strategy for developing new drugs while protecting natural resources. Future research should focus on innovative cultivation techniques, com-munity-led conservation efforts, and advanced analytical methods to enhance the discovery of new drugs. The adoption of agroecological practices, technological advancements, and policy support will be crucial in ensuring sustainable and equitable benefits for healthcare and agriculture. Bridging tra-ditional knowledge with scientific research will foster new therapeutic discoveries while promoting environmental sustainability.