AI-Powered Antibiotic Discovery: Microbial Genome Mining, Biosynthetic Gene Clusters & Natural Product Prioritization

The growing challenge of antimicrobial resistance (AMR) has accelerated the search for novel antibiotics from microbial natural products. Advances in microbial genomics, bioinformatics, and artificial intelligence are transforming how researchers identify and investigate potential antibiotic-producing biosynthetic pathways. This three-day, hands-on workshop introduces computational approaches for discovering promising microbial natural products through genome mining and AI-assisted analysis. Participants will explore microbial genome datasets, identify biosynthetic gene clusters (BGCs), investigate secondary metabolite pathways, and prioritize potential antibiotic-associated candidates using freely accessible bioinformatics tools. Through guided practical sessions using Google Colab, antiSMASH, MIBiG, and Python, participants will develop research-oriented skills in microbial genome analysis, biosynthetic pathway characterization, and computational antibiotic discovery.

To equip researchers and industry professionals with practical knowledge and computational skills in microbial genome mining, biosynthetic gene cluster analysis, and AI-assisted prioritization of promising natural product candidates for antibiotic discovery.

• Understand the role of microbial natural products and biosynthetic gene clusters in antibiotic discovery.
• Explore microbial genome databases and retrieve genomic information for computational analysis.
• Identify and characterize PKS, NRPS, and RiPP biosynthetic gene clusters using genome-mining tools.
• Investigate microbial secondary metabolite pathways and assess biosynthetic diversity.
• Perform comparative BGC analysis using antiSMASH and MIBiG reference data.
• Understand applications of machine learning and AI in natural product discovery.
• Apply data-driven approaches to prioritize promising antibiotic-associated biosynthetic gene clusters.
• Develop reproducible bioinformatics workflows using Google Colab and Python.

📅 Day 1: Microbial Genome Mining & Biosynthetic Gene Cluster Discovery

• Introduction to AI-powered antibiotic discovery and antimicrobial resistance (AMR) Microbial natural products and secondary metabolites as sources of new antibiotics Antibiotic-producing microorganisms: Streptomyces , Bacillus , and other promising microbial taxa Microbial genome retrieval using NCBI Genome and RefSeq Fundamentals of biosynthetic gene clusters (BGCs) and secondary metabolite pathways Identification of PKS, NRPS, and RiPP biosynthetic gene clusters Genome mining and functional annotation using antiSMASH and GECCO

📅 Day 2: Natural Product Discovery, BGC Annotation & Novelty Assessment

• Microbial biosynthetic pathways involved in natural product and antibiotic production Identification of antibiotic-associated BGCs and specialized metabolite families Genome-based discovery of bacteriocins and antimicrobial peptides Reference-based BGC annotation using the MIBiG database Comparative biosynthetic gene cluster analysis and gene cluster families Introduction to BiG-SCAPE for BGC similarity networking and comparative analysis Assessing biosynthetic novelty through reference similarity, cluster diversity, and genomic context Linking microbial biosynthetic potential with known natural product classes

📅 Day 3: AI-Assisted Antibiotic Candidate Prioritization & Predictive Analysis

• Role of artificial intelligence and machine learning in genome-guided antibiotic discovery Integrating BGC annotations, biosynthetic classes, and reference similarity data Introduction to machine learning approaches for natural product discovery Applications of Random Forest and XGBoost in biological data analysis Feature engineering from BGC characteristics and genomic annotations Multi-parameter prioritization of promising antibiotic-associated BGCs Data visualization and interpretable candidate ranking Experimental validation strategies: metabolomics, antimicrobial activity screening, and compound characterization

• Upon successful completion of the workshop, participants will be able to:Perform Microbial Genome Mining:Retrieve and analyze microbial genomic data to explore antibiotic biosynthetic potential.Identify Biosynthetic Gene Clusters:Detect and classify BGCs associated with microbial secondary metabolite production.Analyze Natural Product Pathways:Investigate biosynthetic diversity and compare gene clusters with characterized references.Evaluate Potential Novelty:Identify potentially distinct biosynthetic pathways using comparative genomic analysis.Apply AI-Assisted Prioritization:Integrate genomic features and computational screening parameters to rank promising BGC candidates.Develop Practical Bioinformatics Skills:Use Google Colab, Python, antiSMASH, and MIBiG for reproducible research workflows.Support Future Antibiotic Research:Generate computational findings that can guide subsequent experimental screening and natural product characterization.
• Perform Microbial Genome Mining:Retrieve and analyze microbial genomic data to explore antibiotic biosynthetic potential.
• Identify Biosynthetic Gene Clusters:Detect and classify BGCs associated with microbial secondary metabolite production.
• Analyze Natural Product Pathways:Investigate biosynthetic diversity and compare gene clusters with characterized references.
• Evaluate Potential Novelty:Identify potentially distinct biosynthetic pathways using comparative genomic analysis.
• Apply AI-Assisted Prioritization:Integrate genomic features and computational screening parameters to rank promising BGC candidates.
• Develop Practical Bioinformatics Skills:Use Google Colab, Python, antiSMASH, and MIBiG for reproducible research workflows.
• Support Future Antibiotic Research:Generate computational findings that can guide subsequent experimental screening and natural product characterization.

This workshop is designed for researchers, academicians, and industry professionals interested in microbial natural products, genomics, and computational antibiotic discovery. Researchers & PhD Scholars: Working in Microbiology, Biotechnology, Microbial Genomics, Natural Products, and Antibiotic Discovery. Academicians & Faculty Members: Specializing in Molecular Biology, Bioinformatics, Microbial Biotechnology, and Pharmaceutical Sciences. Postdoctoral Researchers: Investigating microbial secondary metabolites, biosynthetic pathways, and antimicrobial compounds. Pharmaceutical & Biotechnology Professionals: Involved in antimicrobial drug discovery, natural product research, and computational R&D. Bioinformatics & Computational Biology Researchers: Interested in genome mining, biosynthetic gene cluster analysis, and AI-assisted molecular discovery. Natural Product & Medicinal Chemistry Researchers: Exploring microbial metabolites and potential bioactive compounds for therapeutic development.