PhytoMed AI: From Plant Bioactives to Drug Leads
From Medicinal Plants to Safer Drug Candidates
About This Course
Medicinal plants are an important source of diverse bioactive compounds and continue to play a significant role in natural-product research and drug discovery.
This 3-day virtual hands-on workshop introduces participants to computational phytomedicine and will learn how to select a medicinal plant, identify its major phytochemicals, validate reported compounds using scientific literature, and retrieve relevant molecular information. The workshop will then cover ADME and drug-likeness profiling, including physicochemical properties, gastrointestinal absorption, blood-brain barrier permeability, bioavailability, and structural alerts.
Participants will also evaluate key toxicity endpoints such as LD50, hepatotoxicity, mutagenicity, carcinogenicity, cytotoxicity, and organ toxicity. By integrating phytochemical, pharmacokinetic, and safety data, participants will systematically compare compounds and prioritize phytochemical leads with more favorable profiles for further research and therapeutic development.
Aim
To provide practical training in phytochemical profiling, ADME assessment, toxicity prediction, and computational lead prioritization.
Workshop Objectives
By the end of the workshop, participants will be able to:
- Understand the principles of computational phytomedicine and phytochemical profiling
- Identify major phytochemicals reported in medicinal plants
- Distinguish qualitative, quantitative, and literature-supported phytochemical evidence
- Retrieve molecular structures and physicochemical properties of phytochemicals
- Evaluate ADME and drug-likeness characteristics
- Predict major toxicity endpoints using in silico tools
- Compare ADME and toxicity profiles across multiple phytochemicals
- Integrate pharmacokinetic and safety evidence for phytochemical lead prioritization
Workshop Structure
📅 Day 1: Medicinal Plant Profiling & Evidence-Based Phytochemical Discovery
- Introduction to computational phytomedicine
- Medicinal plants as sources of bioactive compounds
- Plant-part-specific phytochemical distribution
- Qualitative vs quantitative phytochemical evidence
- Literature-supported compound selection
- Principles of phytochemical database mining
- Molecular identifiers, structures, and chemical information
- Criteria for prioritizing major phytochemicals
🧪 Hands-On: Building a Curated Phytochemical Dataset
- Identify medicinal plant compounds, validate reported phytochemicals through scientific literature, and retrieve molecular information.
- Medicinal Plant → Reported Phytochemicals → Literature Validation → Molecular Data
🧰 Tools: IMPPAT | PubMed | PubChem | Google Colab | Python | pandas
📅 Day 2: ADME, Drug-Likeness & Pharmacokinetic Profiling
- Key physicochemical properties influencing absorption, distribution, metabolism, and excretion (ADME)
- Lipinski and Veber rules for assessing compound drug-likeness
- Evaluating solubility and lipophilicity of phytochemicals
- Assessing gastrointestinal absorption and potential oral availability
- Predicting blood-brain barrier (BBB) permeability for CNS applications
- Understanding P-glycoprotein (P-gp) transport and efflux interactions
- Analyzing CYP450 metabolism, metabolic liabilities, and potential drug interactions
- Interpreting bioavailability-related parameters for comparative pharmacokinetic suitability
🧪 Hands-On: ADME & Drug-Likeness Evaluation
- Evaluate selected phytochemicals for predicted ADME behavior, drug-likeness, and comparative compound ranking.
- ADME Profile → Drug-Likeness Comparison → Compound Ranking
🧰 Tools: PubChem | SwissADME | Google Colab | Python | RDKit | pandas | matplotlib
📅 Day 3: Computational Toxicology & Safety-Guided Lead Prioritization
- Understanding LD50 values and toxicity classes for compound safety evaluation
- Assessing hepatotoxicity risks associated with selected phytochemicals
- Evaluating mutagenicity predictions and potential genetic toxicity
- Examining carcinogenicity predictions for long-term safety risk assessment
- Analyzing cytotoxicity endpoints and potential harmful cellular effects
- Assessing skin sensitization potential and adverse dermal reactions
- Evaluating organ toxicity predictions across major physiological systems
- Integrating multiple toxicity endpoints for consensus-based safety assessment
🧪 Hands-On: Toxicity Prediction & Safety-Guided Lead Prioritization
- Predict major toxicity endpoints, compare safety profiles, integrate findings with ADME results, and classify compounds as Preferred, Caution, or Low Priority.
- Toxicity Profile → Safety Matrix → Final Prioritized Phytochemical Leads
🧰 Tools: ProTox 3.0 | pkCSM | Google Colab | Python | pandas | matplotlib | scikit-learn
Who Should Enrol?
This work is suitable for:
- PhD Scholars & Research Scholars
- Academicians & Faculty Members
- Herbal medicine Researchers
- Bioinformatics & Computational Biology Researchers
- Pharmacology & Pharmaceutical Science Researchers
- Drug Discovery & Development Scientists
- Precision Medicine Researchers
- Biotechnology & Life Science Professionals
- Clinical Research Professionals
- Pharmaceutical and Biotechnology Industry Professionals
Important Dates
Registration Ends
October 20, 2026
IST 7:00 PM
Workshop Dates
October 20, 2026 – October 22, 2026
IST 7:30 PM
Workshop Outcomes
Participants will be able to:
- Identify phytochemicals from medicinal plants
- Build a curated phytochemical dataset
- Analyze ADME and drug-likeness properties
- Predict key toxicity endpoints
- Compare multiple compounds systematically
- Prioritize safer and pharmacokinetically favorable phytochemical leads
Fee Structure
Student Fee
₹1999 | $65
Ph.D. Scholar / Researcher Fee
₹2999 | $75
Academician / Faculty Fee
₹3999 | $85
Industry Professional Fee
₹4999 | $95
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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