Dermatophytosis is among the most widespread fungal infections worldwide, affecting the keratinized tissues of the skin, hair, and nails and posing a persistent challenge to public health. Despite the availability of conventional antifungal drugs, treatment outcomes are often compromised by prolonged therapy, recurrence, adverse effects, and the growing threat of antifungal resistance. These limitations have accelerated the search for novel, nature-inspired therapeutics capable of offering safer and more effective disease management. In this context, medicinal plants have emerged as valuable sources of multifunctional bioactive compounds with significant antifungal potential. Azadirachta indica A. Juss. (Neem), a cornerstone of traditional medicine, has gained increasing scientific attention as a promising plant-derived therapeutic against dermatophytosis. Its pharmacological efficacy is attributed to a diverse array of phytochemicals, including azadirachtin, nimbidin, nimbin, gedunin, salannin, quercetin, and other limonoids, which collectively exhibit antifungal, anti-inflammatory, antioxidant, and skin-protective activities. Unlike conventional agents that primarily target fungal growth, neem-derived compounds offer a multi-target approach by inhibiting dermatophyte proliferation, reducing oxidative stress, modulating inflammatory responses, and promoting tissue regeneration. Recent in vitro and in vivo studies have demonstrated significant activity of neem extracts and formulations against major dermatophytes, including Trichophyton, Microsporum, and Epidermophyton species. Furthermore, advances in herbal nanotechnology, such as nanoemulsions and nanogels, have enhanced the delivery and therapeutic performance of neem bioactives. This review comprehensively examines the phytochemistry, antifungal mechanisms, experimental evidence, and future translational prospects of Azadirachta indica, highlighting its potential as a next-generation botanical strategy for the sustainable management of dermatophytosis.

Natural products are still vital sources of structurally diverse and biologically active molecules; however, these products are often hard to discover due to the complexity of natural extracts and the isolation of known metabolites. Phytochemical dereplication is an effective tool for identification of known constituents prior to extensive purification and the selection of metabolites to study for possible chemical or biological novelty. The ability for rapid phytochemical investigation has greatly increased in the last few years with the development of liquid chromatography–tandem mass spectrometry (LC–MS/MS), molecular networking (MN), and artificial intelligence (AI). The accurate mass, chromatographic and fragmentation data obtained by LC–MS/MS is then used to correlate similar metabolites into molecular families through molecular networking, and to provide chemical context for unannotated features. Additional support can be provided by machine learning (ML) and AI for spectral classification, chemical-class prediction, candidate-structure generation, similarity assessment and bioactivity based prioritization. The integration then enables metabolite detection, computational metabolite annotation, molecular family analysis, metabolite candidate ranking, targeted isolation and experimental validation to be coupled together in a workflow. However, some important limitations include incomplete spectral libraries, structural isomerism, instrumental variability, training-data bias, limited model interpretability and false annotation. This means that the predictions made from a computer model should be interpreted as a structural hypothesis, unless backed up by suitable experimental evidence. This review explores how LC–MS/MS, molecular networking and AI/ML complement each other in the process of phytochemical dereplication, how they are used in the discovery of natural products, and the challenges and opportunities of explainable AI, multimodal data integration, better spectral databases, and increasingly automated dereplication workflows.

Herbal medicines represent a valuable source of therapeutically active compounds and are increasingly incorporated into modern healthcare and drug discovery. However, their chemically complex composition, multi-target pharmacological effects, batch-to-batch variability, metabolic transformation, potential toxicity, and herb–drug interactions present significant challenges for systematic safety assessment. Conventional toxicological approaches are often time-consuming, costly, and dependent on extensive experimental studies, highlighting the need for predictive and mechanism-oriented strategies. Artificial Intelligence (AI), including Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP), has emerged as a promising approach for advancing herbal toxicology and safety evaluation. This review critically examines the application of AI-assisted approaches for toxicity prediction, absorption, distribution, metabolism, excretion, and toxicity (ADMET) assessment, herb–drug interaction prediction, mechanistic toxicology, and integrated safety profiling of herbal medicines. Particular attention is given to quantitative structure–activity relationship (QSAR) modelling, molecular docking, virtual screening, pharmacokinetic prediction, multi-omics integration, knowledge graphs, adverse-effect prediction, and explainable AI. These approaches can facilitate the early identification of potentially hazardous phytochemicals, predict organ-specific toxicity, identify toxicophoric structural features, evaluate metabolic liabilities, and characterize interactions with drug-metabolizing enzymes and transporters. Furthermore, integration of chemical, biological, pharmacological, clinical, and toxicological datasets may improve the prioritization of safer herbal candidates and support mechanism-informed risk assessment. Despite these advances, limitations related to data quality, dataset heterogeneity, model interpretability, external validation, uncertainty, and regulatory acceptance remain important challenges. Future integration of explainable AI, standardized toxicological datasets, multi-omics technologies, digital models, and experimental validation could establish more reliable and predictive frameworks for herbal medicine safety evaluation.