AI-Driven Phage Therapy: Designing Precision Antimicrobial Solutions Against Drug Resistance

This 3-day hands-on workshop introduces participants to AI-assisted approaches for modern phage therapy research. It covers computational phage–host prediction, rational phage cocktail design, genomic safety screening, and discovery of phage-derived antimicrobial proteins. Participants will work with genomic and protein sequence data using practical Python-based workflows that can be executed in Google Colab.

To provide researchers and professionals with practical skills to apply bioinformatics and machine learning in phage therapy, host prediction, cocktail optimisation, and antimicrobial candidate discovery.

• Understand the role of bacteriophages in combating antimicrobial resistance.
• Analyse phage genomic data for host prediction.
• Apply machine learning for phage–host classification.
• Design rational and resistance-aware phage cocktails.
• Screen phages for therapeutic suitability and genomic safety.
• Identify and prioritise phage-derived antimicrobial proteins.
• Develop reproducible computational workflows using Google Colab.

📅 Day 1: AI-Based Phage–Host Prediction

• Introduction to bacteriophage therapy and antimicrobial resistance Lytic vs temperate phages and therapeutic suitability Understanding phage–host specificity and host range Genomic signals for host prediction: k-mer patterns GC content CRISPR spacer matches sequence similarity codon usage Introduction to AI/ML-based phage–host prediction Overview of tools: iPHoP, CHERRY, PhaBOX and RaFAH Prediction confidence and interpretation

🗓️ Day 2: Rational Phage Cocktail Design

• Why single-phage therapy may fail Host-range and bacterial susceptibility matrices Phage resistance and receptor diversity Genomic safety screening of candidate phages Removing temperate, virulence and AMR-associated candidates Host coverage and redundancy analysis Resistance-aware phage selection Set-cover and optimisation approaches for cocktail design Phage–antibiotic synergy and sequential therapy concepts

🗓️ Day 3: AI-Guided Phage-Derived Antimicrobial Discovery

• Phages as sources of antimicrobial molecules Endolysins and phage-derived lysins Depolymerases and biofilm-targeting enzymes Protein sequence-based antimicrobial discovery Amino-acid and physicochemical feature extraction Introduction to protein embeddings and protein language models AI-based classification of phage proteins Candidate scoring and prioritisation Translational applications in AMR and precision antimicrobial therapy

• Predict probable bacterial hosts from phage genomic features.
• Interpret host-range and prediction confidence data.
• Construct phage–bacteria interaction matrices.
• Optimise multi-phage cocktails based on host coverage and resistance considerations.
• Apply genomic safety filters to candidate phages.
• Analyse phage protein sequences for antimicrobial potential.
• Prioritise endolysin and depolymerase candidates using AI/ML approaches.
• Apply computational phage therapy workflows in research projects.

This workshop is suitable for: Researchers and Research Scholars PhD and Postgraduate Students Academicians and Faculty Members Microbiologists and Molecular Biologists Bioinformatics and Computational Biology Researchers Antimicrobial Resistance Researchers Infectious Disease Researchers Biotechnology and Pharmaceutical Professionals Researchers working in Phage Biology, Microbial Genomics, Viromics and Alternative Antimicrobial Strategies