OSADHI

OSADHI consolidates medicinal plant records, phytochemical profiles, and geographical distribution data from India to support phytochemical characterization and natural product-based drug discovery.

Key Features:

• Dataset scope: Contains 6,959 unique medicinal plants spanning 348 families recorded across 28 states and 8 union territories in India.
• Data sections: Organizes information into four sections: Traditional Knowledge (taxonomy and vernacular names), Geographical Indications (geographical availability), Phytochemicals, and Chemoinformatics.
• Phytochemical catalog: Curates 27,440 unique phytochemicals with detailed information available for 22,314 compounds.
• Chemoinformatics analyses: Computes physicochemical properties and ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) profiles using open-source web servers and in-house Python scripts.
• Phytochemical classification: Classifies phytochemicals using NPClassifier into class, superclass, and pathways.
• Predictive analytics: Generates antiviral-potency predictions for phytochemicals using Random Forest and XGBoost machine-learning models.

Scientific Applications:

• Ethnobotany and pharmacognosy research: Provides a consolidated resource for researchers studying traditional uses and taxonomy of Indian medicinal plants.
• Plant-based therapeutic exploration: Enables investigation of phytochemical composition and traditional usage to prioritize plant-derived candidates for therapeutic research.
• Lead identification and development: Supports identification and development of novel therapeutic agents derived from medicinal-plant phytochemicals.

Methodology:

Chemoinformatics analyses use open-source web servers and in-house Python scripts to compute physicochemical properties and ADMET profiles; phytochemical classification uses NPClassifier; antiviral-potency prediction uses Random Forest and XGBoost.