Neurodegenerative disorders, including Alzheimer’s disease (AD), Parkinson’s disease (PD), Huntington’s disease (HD), and amyotrophic lateral sclerosis (ALS), arise from highly interconnected molecular and cellular abnormalities that progressively lead to neuronal dysfunction, synaptic failure, and cell death. This review provides a unified framework to understand the interrelated molecular mechanisms driving these diseases, with a focus on identifying key disease-specific intervention nodes. Core contributors include oxidative stress, mitochondrial dysfunction, protein aggregation, neuroinflammation, and emerging roles of peroxisomal dysfunction in redox imbalance, lipid dysregulation, and inflammatory amplification. Single-target therapies often show limited efficacy due to the complex, interconnected nature of these pathways. In contrast, polypharmacology, which targets multiple disease-relevant mechanisms simultaneously, offers a more promising therapeutic strategy. This review critically examines how pathway crosstalk drives neurodegenerative progression, with particular emphasis on mitochondrial–ROS–inflammatory signaling, aggregation–proteostasis failure, synaptic–neuroimmune dysfunction, and gut–brain communication. It evaluates various multi-node intervention strategies, including multi-target-directed ligands (MTDLs), molecular hybrids, natural products, drug repurposing, and nanocarrier-based delivery systems. Advances in network pharmacology, artificial intelligence (AI), bioinformatics, and multi-omics have enhanced the identification of actionable therapeutic nodes, candidate compounds, and brain-targeted delivery platforms. Notably, the NOD-like receptor pyrin domain-containing protein 3 (NLRP3) inflammasome and cyclic GMP–AMP synthase (cGAS)—stimulator of interferon genes (STING) pathways—play distinct roles in neuroinflammation, amplifying neuronal damage by releasing inflammatory cytokines and inducing mitochondrial dysfunction. However, successful translation into clinical practice remains constrained by challenges such as blood–brain barrier penetration, patient heterogeneity, and biomarker limitations. The review advocates for a shift towards mechanism-informed, patient-stratified polypharmacological strategies to better address the network pathology of neurodegeneration, despite significant translational hurdles.

Major depressive disorder (MDD) is a mental illness with high mortality, suicide, and relapse rates that could become the leading cause of health problems worldwide by 2030. The microbiota–gut–brain axis involves bidirectional communication between the human gut microbiota and the central nervous system (CNS). The gut microbiome is a complex ecosystem of approximately 100 trillion microorganisms, including viruses, bacteria, and fungi. The gut microbiota has recently been recognized for its impact on various diseases and health concerns. Several factors influence the composition and structure of gut microbes, ultimately affecting human physiology, with the nervous system being particularly vulnerable. The gut–brain–microbiota axis influences several important brain functions through numerous pathways, including vagus nerve signaling, gut microbial synthesis of metabolites, and immune-related chemicals. These factors can influence neurotransmitter activity, neuroinflammation, behavior, and mental health. Despite increased interest, the possibility of modifying the gut microbiota as a therapeutic approach remains unclear. Although numerous studies suggest that microbiota play an important role in many illnesses, the precise mechanisms are yet to be elucidated, and there are currently no evidence-based, microbiota-focused treatments for these illnesses. Recent research indicates that gut dysbiosis (GD) causes increased intestinal permeability (leaky gut), initiates systemic inflammation, and contaminates the blood. Opportunistic microbial metabolites cross the blood–brain barrier, triggering a neuroinflammatory cascade and apoptotic pathways while affecting neurogenesis and neurotransmitters, ultimately resulting in the development of MDD and anxiety. This review examined the factors influencing normal gut microbiota and GD-mediated MDD, as well as possible therapeutic options. The study outlines its objectives and methodological approaches, including the screening and filtering of research on GD-induced depression. Furthermore, it explored the daily use of dietary supplements, revealing new paths for clinical and preclinical research.

Artificial intelligence (AI) is increasingly used in cancer research, enabling integrative analysis of complex biomedical data to identify actionable therapeutic vulnerabilities. This review specifically examines how AI advances mechanistic cancer target discovery and translational drug development, focusing on: (1) the processing of large-scale genomics, transcriptomics, proteomics, metabolomics, single-cell profiling, spatial, and clinical datasets using machine learning (ML) and deep learning (DL) algorithms; (2) the identification of candidate biomarkers, driver genes, dysregulated pathways, tumor dependencies, and molecular targets that traditional methods often miss; (3) the integration of multi-omics data, network biology, causal inference, and systems-level modeling to refine mechanistic understanding of cancer progression and separate functional driver events from passengers; and (4) applications in drug development, including virtual screening, molecular modeling, structure-informed target validation, drug repurposing, synthetic lethality prediction, and de novo drug design, which collectively may enhance early-stage drug discovery efficiency. The review underscores that AI serves as both a predictive tool and a platform for linking molecular mechanisms to hypothesis generation, target prioritization, and rational treatment design. Challenges such as data heterogeneity, algorithmic bias, interpretability, reproducibility, regulatory requirements, and patient privacy must be addressed for robust translation and clinical use. Future directions may focus on hybrid approaches that integrate causal modeling, explainable AI, multimodal data, and experimental validation to yield mechanistically grounded, clinically actionable insights. AI-driven approaches ultimately aim to accelerate mechanism-based cancer target discovery and enable more precise, biologically informed anticancer therapies.

Background/Objectives: Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder associated with insulin resistance, β-cell dysfunction, and systemic complications. Methods: In this preliminary study, the metabolic effects of BE-FD-1, a water extract of Raphanus sativus L. leaves cultivated under a mineral-fortification protocol, were investigated in a high-fat-diet/streptozotocin (HFD/STZ)-induced diabetic mouse model. Inductively coupled plasma mass spectrometry analysis confirmed the presence of vanadium, chromium, magnesium, zinc, and calcium in radish leaf. Male C57BL/6 mice (n = 5/group) were orally administered BE-FD-1 at 250 or 500 mg/kg once daily for four weeks, with metformin (250 mg/kg) as a positive reference. Results: BE-FD-1 at 500 mg/kg significantly reduced the oral glucose tolerance test area under the curve and fasting blood glucose levels, significantly restored serum insulin levels, and significantly decreased serum ALT, triglyceride, and total cholesterol levels relative to the HFD/STZ control group. Body weight gain and AST showed non-significant decreasing tendencies. Serum creatinine remained within the normal range, providing a preliminary safety signal that should be interpreted with caution given the absence of additional renal biomarkers and histopathological evaluation. Conclusions: These exploratory findings suggest that BE-FD-1 may warrant further investigation as a candidate functional ingredient for T2DM-related metabolic dysfunction; however, larger studies with comprehensive phytochemical characterization, mechanistic validation, and broader safety evaluation are required.

Background: Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by the selective loss of dopaminergic neurons in the substantia nigra. Oxidative stress, neuroinflammation, and α-synuclein aggregation are central pathological features of PD. Zanthoxylum piperitum DC, commonly known as Korean pepper or chopi, is a traditional dietary spice in Eastern Asia and has been reported to possess antioxidant and anti-inflammatory properties. This study investigated the neuroprotective and motor function–enhancing effects of distilled extract of Z. piperitum (deZP) in 1-Methyl-4-phenylpyridinium (MPP⁺)-treated Caenorhabditis elegans and 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced mouse models of PD. Methods: In the C. elegans model, dopaminergic neurotoxicity was induced by MPP⁺, and deZP was tested at 0.25, 0.5, and 1% (v/v) to evaluate neuronal preservation through GFP-labeled dopaminergic neurons and α-synuclein expression. Concurrently, in the MPTP-induced mouse model, deZP was administered intranasally at a fixed dose of 20 μL/mouse, equivalent to 5 mg/mouse. Motor function was assessed using the rota-rod test, pole test, and grip strength test, while dopaminergic neuronal survival was evaluated by tyrosine hydroxylase (TH) immunostaining. Results: In MPP⁺-treated C. elegans, deZP significantly restored green fluorescent protein (GFP) fluorescence in dopaminergic neurons and reduced α-synuclein expression, with the most pronounced effects observed at 1% (v/v). In the MPTP-induced mouse model, deZP at this fixed intranasal dose significantly improved motor performance and preserved TH-positive neurons in the substantia nigra. Conclusions: These findings suggest that deZP may represent a promising preclinical candidate for further investigation in PD-related neurodegeneration.

Background/Objectives: Psoriasis is a chronic immune-mediated inflammatory skin disease characterized by keratinocyte hyperproliferation and systemic inflammatory responses, which are primarily driven by the interleukin (IL)-23/Th17 axis. Although current therapies effectively suppress inflammation, their long-term use is often limited by adverse systemic effects, underscoring the need for safe immunomodulatory agents. This study investigated the anti-psoriatic efficacy of J2H-1802, a novel hybrid compound combining mycophenolate mofetil (MMF) and 5-aminosalicylic acid (5-ASA), in an imiquimod (IMQ)-induced psoriasis-like mouse model. Methods: J2H-1802 was orally administered at doses of 125 and 250 mg/kg during IMQ treatment, and its effects were evaluated by conducting clinical assessments, histological analyses, and inflammatory cytokine measurements in the serum and skin tissues. Results: J2H-1802 treatment reduced Psoriasis Area and Severity Index (PASI) scores, skin and ear thickness, and splenomegaly in a dose-dependent manner. Histological examination revealed IMQ-induced epidermal hyperplasia attenuation and dermal collagen organization improvement. In addition, J2H-1802 significantly reduced serum tumor necrosis factor-α (TNF-α) levels and suppressed pro-inflammatory cytokine expression, including IL-1β, IL-6, IL-17, and TNF-α, in psoriatic skin. Conclusions: J2H-1802 alleviates both local and systemic inflammatory features of psoriasis, suggesting its potential as a therapeutic candidate for targeting IL-23/Th17-mediated inflammatory pathways.