PhytoMed AI: AI-Guided Phytochemical Discovery, ADMET Screening & Drug Lead Prioritization

To provide practical training in phytochemical profiling, ADME assessment, toxicity prediction, and computational lead prioritization.

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

Day 1: AI-Assisted Phytochemical Discovery & Molecular Characterization

Topics Covered:

• Introduction to AI-driven phytochemical research and natural product drug discovery.
• Medicinal plants, secondary metabolites, and bioactive compounds in therapeutic research.
• Phytochemical identification and data retrieval using IMPPAT, PubChem, and scientific literature.
• Molecular structure representation using SMILES and compound standardization.
• Physicochemical descriptor analysis: molecular weight, LogP, TPSA, and hydrogen-bond properties.
• Drug-likeness assessment using Lipinski’s Rule of Five.
• Introduction to RDKit for molecular characterization and chemical space analysis.

Hands-On: Phytochemical Library Development Using Google Colab

Retrieve and analyze selected phytochemicals, calculate molecular descriptors using RDKit, and prepare a structured compound library for further screening.

Tools: Google Colab, Python, PubChem, IMPPAT, RDKit, pandas, IMPPAT,

Expected Outcome: Phytochemical dataset with molecular descriptors and preliminary drug-likeness profiles.

Day 2: AI-Powered ADMET Prediction & Toxicity Screening

Topics Covered:

• Importance of ADMET profiling in phytochemical-based drug discovery.
• Computational drug-likeness and pharmacokinetic assessment.
• Absorption and distribution: gastrointestinal absorption, BBB permeability, and P-glycoprotein.
• Metabolism and excretion: CYP450 interactions and clearance-related predictions.
• AI-assisted toxicity evaluation: hepatotoxicity, mutagenicity, and cardiotoxicity-related risks.
• Introduction to SwissADME, ProTox 3.0, and ADMETlab 3.0.
• Interpretation of computational predictions for early-stage drug candidate screening.

Hands-On: ADMET & Toxicity Profiling of Phytochemicals

Evaluate selected compounds using SwissADME and ProTox 3.0, then compare drug-likeness, pharmacokinetic, and toxicity predictions in Google Colab.

Tools: Google Colab, SwissADME, ProTox 3.0, ADMETlab 3.0, pandas.

Expected Outcome: Comparative ADMET and toxicity screening report with shortlisted phytochemical candidates.

Day 3: AI-Driven Drug Lead Prioritization & Predictive Analytics

Topics Covered:

• Structure-based virtual screening and phytochemical–protein interactions.
• Disease-associated protein target identification using UniProt and RCSB PDB.
• Protein–ligand preparation and molecular docking using AutoDock Vina.
• 2D/3D visualization of binding interactions using BIOVIA Discovery Studio Visualizer.
• Docking score interpretation, binding poses, and hydrogen-bond analysis.
• Integration of molecular descriptors, ADMET, toxicity, and docking results.
• AI-based molecular property prediction using QSAR, Random Forest, and XGBoost.
• Explainable AI using SHAP and Python-based phytochemical lead prioritization.

Hands-On: AI-Assisted Phytochemical Lead Ranking Using Google Colab

Identify a disease-associated protein target using UniProt and RCSB PDB, perform molecular docking of selected phytochemicals using AutoDock Vina, and visualize binding interactions using BIOVIA Discovery Studio Visualizer. Integrate docking scores, molecular descriptors, ADMET, and toxicity data in Google Colab to explore AI-based predictions and prioritize potential drug leads.

Tools: Google Colab, Python, pandas, NumPy, scikit-learn, matplotlib, AutoDock Vina, BIOVIA Discovery Studio, QSAR, Random Forest, and XGBoost

Expected Outcome: Prioritized phytochemical lead candidates with comparative ranking charts and a reproducible computational workflow.

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

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