AI-Driven Microbial Natural Product Discovery: Genome Mining & Antibiotic Discovery
From Microbial Genomes to AI-Prioritized Antibiotic Candidates
About This Course
This advanced 3-day workshop introduces participants to computational discovery of microbial natural products and potential antibiotic candidates using microbial genome mining and AI-assisted analysis. Participants will work with publicly available microbial genomic data and open/free computational resources to identify biosynthetic potential, explore biosynthetic gene clusters, and prioritize promising antibiotic candidates.
Aim
To provide participants with an advanced, hands-on understanding of how microbial genome mining, biosynthetic gene cluster (BGC) analysis, and AI-assisted approaches can be integrated to discover and prioritize potential novel antibiotic candidates from microbial genomes.
Workshop Objectives
By the end of the workshop, participants will be able to:
- Understand the role of microbial natural products in antibiotic discovery.
- Explain the principles and workflow of microbial genome mining.
- Identify and interpret biosynthetic gene clusters (BGCs) associated with natural-product biosynthesis.
- Use open/free computational tools such as NCBI, Galaxy, antiSMASH, BLAST, UniProt and MIBiG for genome and BGC analysis.
- Evaluate BGCs for their potential to produce antimicrobial and other bioactive compounds.
- Apply basic AI/ML-assisted approaches for prioritizing promising antibiotic candidates.
- Interpret computational predictions and distinguish promising candidates from lower-priority candidates.
- Develop a computationally informed experimental validation strategy for shortlisted antibiotic candidates.
Workshop Structure
Day 1 — Microbial Genome Mining & Biosynthetic Potential
Focus: From microbial genome to natural-product discovery
Topics:
- Microbial natural products as a source of new antibiotics
- Why genome mining is important for discovering hidden biosynthetic potential
- Understanding biosynthetic gene clusters (BGCs)
- Selecting and preparing a microbial genome for analysis
- Introduction to genome-mining workflows
Hands-On 1:
Genome Mining for Biosynthetic Gene Clusters
Participants will work with a publicly available microbial genome and perform genome mining to identify potential BGCs and associated natural-product classes.
Free/Open Tools:
NCBI • Galaxy • antiSMASH
Day 2 — BGC Analysis & Antibiotic Candidate Identification
Focus: Understanding and evaluating discovered biosynthetic gene clusters
Topics:
- Interpreting BGC architecture and gene organization
- Functional annotation of biosynthetic genes
- Linking BGCs with known natural products
- Detecting potentially novel biosynthetic regions
- Comparing BGCs across microbial genomes
- Identifying BGCs with potential antimicrobial relevance
Hands-On 2:
Biosynthetic Gene Cluster Characterization & Antibiotic Candidate Screening
Participants will analyse selected BGCs, investigate their predicted functions and compare them against known biosynthetic clusters to identify promising antibiotic-producing candidates.
Free/Open Tools:
antiSMASH • MIBiG • NCBI • BLAST • UniProt
Day 3 — AI-Assisted Candidate Prioritization
Focus: Using AI/ML to prioritize promising antibiotic candidates
Topics:
- Why AI can accelerate natural-product discovery
- Feature extraction from genomic/BGC data
- Introduction to machine-learning-based candidate prioritization
- Building a simple classification/ranking workflow
- Interpreting AI-generated predictions
- From computational prediction to experimental validation
- Research opportunities and limitations
Hands-On 3:
AI-Based Prioritization of Potential Antibiotic Candidates
Participants will use a prepared/public dataset to extract relevant features and build a simple machine-learning workflow for ranking or classifying potential antibiotic candidates.
Free/Open Tools:
Google Colab • Python • Pandas • scikit-learn • antiSMASH outputs
Who Should Enrol?
- PhD Scholars and Research Scholars
- Microbiology Researchers
- Biotechnology Researchers
- Bioinformatics Researchers
- Microbial Genomics Researchers
- Natural Product Researchers
- Antimicrobial & AMR Researchers
- Pharmaceutical R&D Professionals
- Biotechnology & Biopharmaceutical Professionals
- MSc/MTech students in Microbiology, Biotechnology, Bioinformatics, Life Sciences or related disciplines
- Researchers interested in AI-driven drug and antibiotic discovery
Important Dates
Registration Ends
October 19, 2026
IST 4:30 PM
Workshop Dates
October 19, 2026 – October 21, 2026
IST 5:00 PM
Workshop Outcomes
After completing the workshop, participants will be able to:
- Perform a basic microbial genome-mining workflow using publicly available genomic data.
- Detect and analyse biosynthetic gene clusters using genome-mining tools.
- Characterize selected BGCs and assess their potential natural-product classes.
- Generate a shortlist of potential antibiotic candidates based on genomic evidence.
- Apply a basic AI/ML workflow to prioritize candidate compounds/BGCs.
- Interpret AI-assisted predictions in a biological context.
- Prepare a research-ready candidate prioritization report.
- Identify suitable next steps for laboratory validation and further research.
Fee Structure
Student Fee
₹1999 | $60
Ph.D. Scholar / Researcher Fee
₹2999 | $70
Academician / Faculty Fee
₹3999 | $80
Industry Professional Fee
₹4999 | $90
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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