Screening of Natural Compounds Against Multi-Drug Resistant (MDR) Bacteria
Harnessing Nature to Combat Antimicrobial Resistance
About This Course
This three-day workshop (1.5-hour lecture per day) provides a comprehensive understanding of screening natural compounds against multi-drug resistant (MDR) bacteria using experimental and computational approaches. Participants will explore mechanisms of antimicrobial resistance, bioassay-guided screening, molecular docking, and in silico drug discovery tools. Case studies involving pathogens such as Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa will be discussed. The workshop integrates microbiological techniques with bioinformatics, cheminformatics, and AI tools to accelerate the discovery of novel antimicrobial agents from natural sources.
Aim
To provide theoretical knowledge and practical insights into identifying, screening, and evaluating natural compounds as potential therapeutics against multi-drug resistant bacterial pathogens.
Workshop Objectives
- Understand mechanisms of antimicrobial resistance and MDR bacteria.
- Learn sources and extraction methods of bioactive natural compounds.
- Explore in vitro antimicrobial screening methods (MIC, disk diffusion, broth dilution).
- Apply computational tools for molecular docking and target prediction.
- Perform data analysis using Python-based platforms.
- Understand global AMR action frameworks including initiatives by the World Health Organization.
- Evaluate translational challenges in natural product drug development.
Workshop Structure
Day 1: Introduction to MDR Bacteria and Natural Compound Libraries
- MDR Bacteria Overview: Mechanisms of resistance, global impact, and clinical relevance
- Natural Compounds in Antimicrobial Research: Sources (plants, marine, microbial), chemical diversity, and bioactive classes
- Compound Libraries and Data Sources: PubChem, ZINC, NPASS, ChEMBL
- In Silico Screening Concepts: Virtual screening, ligand-based vs structure-based approaches
- Data Preparation: Compound file formats (SDF, SMILES), bacterial target selection, preprocessing
- Tools: Python, Pandas, RDKit, Jupyter/Colab
Day 2: Computational Screening and Molecular Docking
- Molecular Docking Fundamentals: Receptor-ligand interactions, docking algorithms, scoring functions
- Target Preparation: Choosing MDR bacterial proteins (e.g., β-lactamase, efflux pumps), structure retrieval (PDB), preprocessing
- Ligand Preparation: Conformer generation, energy minimization, standardization
- Virtual Screening Workflow: Docking natural compounds against bacterial targets
- Scoring & Ranking: Binding affinity, interaction profile, hydrogen bonds, hydrophobic contacts
- Tools: AutoDock Vina, PyRx, RDKit, PyMOL
Day 3: Post-Screening Analysis and Research-Grade Reporting
- Analysis of Docking Results: Top hits, interaction mapping, and compound-target network visualization
- ADMET & Drug-Likeness Evaluation: Predicting pharmacokinetics and toxicity for selected hits
- Hit Prioritization: Multi-criteria scoring based on binding affinity, ADMET, novelty
- Reproducibility & Reporting: Documenting screening workflow, visualization for publications, and interpretation of results
- Tools: Python (pandas, matplotlib, seaborn, PyMOL), SwissADME (optional), Jupyter/Colab
Who Should Enrol?
- Doctoral Scholars & Researchers: PhD candidates seeking to integrate computational workflows into their molecular research.
- Postdoctoral Fellows: Early-career scientists aiming to enhance their data-driven publication profile.
- University Faculty: Professors and HODs interested in modern bioinformatics pedagogy and tool mastery.
- Industry Scientists: R&D professionals from the Biotechnology and Pharmaceutical sectors transitioning to genomic-driven discovery.
- Postgraduate Students: Final-year PG students looking for specialized research-grade exposure beyond standard curricula.
Important Dates
Registration Ends
02/18/2026
IST 07:00 PM
Workshop Dates
02/18/2026 – 02/20/2026
IST 08:00 PM
Workshop Outcomes
By the end of the workshop, participants will be able to:
- Explain mechanisms of drug resistance in major bacterial pathogens.
- Design experimental strategies for screening natural compounds.
- Interpret antimicrobial susceptibility testing results.
- Perform basic molecular docking and computational analysis.
- Analyze screening datasets using statistical and visualization tools.
- Identify promising lead compounds for further development.
Fee Structure
Student Fee
₹1699 | $65
Ph.D. Scholar / Researcher Fee
₹2699 | $75
Academician / Faculty Fee
₹3699 | $85
Industry Professional Fee
₹4699 | $95
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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