The fascinating Habenaria is the largest terrestrial orchid renowned for its intricate flowers and curative tubers. These orchids are believed to have potential benefits in treating various health conditions such as cough, asthma, kidney disorder, hernia, haematuria, and sexual dysfunction. The Habenaria species have been documented in the traditional system of medicine in the past for their therapeutic value. However, the effectiveness of these species has not been thoroughly investigated, and scientific research on the medicinal properties of this genus is limited. It is important to note that further research is needed to understand fully their probable health benefits and to determine effective doses for human use. The integration of traditional indigenous knowledge with modern research activities has the advantage of discovering highly effective drugs that outperform contemporary synthetic medicines. The present study aims to investigate and provide collective information on the latest developments, particularly about their potential therapeutic applications. The discussion revolves around grouping relevant data on phytochemicals and pharmacological relevance to evaluate the presence of bioactive compounds that could support therapeutic purposes. The review provides valuable insights for researchers conducting plant drug research, especially in this genus, and strategies for their effective conservation. <!----> <!----> <!----> <!---->

Crepidium manikathilum is an endemic orchid species recently discovered in the Southern Western Ghats, known only from its type locality with a single population. To date, this endemic species remains largely under-explored with no conservation efforts. The main objective of this study is to identify a new geographical distribution, assess threat status as per IUCN, revise taxonomic treatment, and produce multiple seedlings of C. manikathilum through in vitro. The field survey was conducted in the Southern Western Ghats region during the period of 2020 to 2024. A new distributional record was documented with two new subpopulations at Kodaikanal and Ooty in Tamil Nadu, India. The extent of occurrence and area of occupancy were calculated as 3979.244 km² and 8 km², respectively. The findings indicate that this species is classified in the Critically Endangered (CR) category. Further, mature pods were collected and tested for asymbiotic seed germination in three basal media. Mitra medium was effective for stage I, and stages II and III were high in MS medium. Asymbiotic seed germination was most successful on B₅ medium with 84%, followed by 48% in Mitra medium after two weeks. The in vitro grown explants produced 16 shoots with the combination of 1.5 mg L⁻¹ meta-Topolin with 0.5 mg L⁻¹ IAA. Well-developed plantlets were subsequently acclimatised with a 56% survival rate. The study provides comprehensive details about C. manikathilum for further conservation and assessment. Graphical abstract

Schoenorchis manilaliana M.Kumar & Sequiera collected during a field expedition to Megamalai Wildlife Sancturary forms a new distributional record for the state of Tamil Nadu. The present collection is a first report from outside the type locality. A brief description, photo-plate and other details of this endemic orchid is presented here.

Background/Objectives: Tuberculosis (TB), caused by Mycobacterium tuberculosis, remains a major global health challenge, exacerbated by the emergence of multidrug-resistant strains and limited efficacy of existing therapies. Given the involvement of multiple essential mycobacterial proteins, multitarget drug discovery represents a rational therapeutic strategy. Methods: In this study, an integrated in silico pipeline combining machine learning–based quantitative structure–activity relationship modeling, graph neural network–driven drug–target affinity prediction, molecular docking, molecular dynamics (MD) simulations, and pharmacokinetic–toxicity profiling was employed to identify potential antitubercular leads from natural products. Results: A curated library of over 0.69 million compounds from the COCONUT database was systematically screened against seven essential M. tuberculosis protein targets. Machine learning and heterogeneous graph neural network models effectively captured complex ligand–protein interaction patterns, enabling high-confidence multitarget prioritization. Structure-based docking and MM-GBSA analyses revealed favorable binding affinities, further supported by 100 ns Molecular Dynamics simulations demonstrating stable binding and conformational integrity. In silico ADMET and toxicity predictions identified pharmacokinetically balanced candidates, while density functional theory calculations corroborated favorable electronic properties. Conclusions: Notably, a myricetin-based flavonoid glycoside exhibited consistent multitarget binding and dynamic stability across all targets. Overall, this study underscores the potential of integrated artificial intelligence and structure-based approaches in accelerating natural product-based antitubercular drug discovery and supports further experimental validation of prioritized leads.