Antimicrobial peptides
Antimicrobial Peptide Design for Drug-Resistant Pathogens
Antimicrobial resistance is one of the most urgent challenges in medicine. Antimicrobial peptides offer a mechanistically distinct approach to infection — but designing AMPs with the right balance of potency, selectivity, and stability requires navigating a complex sequence space. We apply AI-assisted computational methods to accelerate AMP discovery and optimization.
The AMP Design Challenge
Antimicrobial peptides must achieve potent activity against target pathogens while maintaining selectivity over host cells. Achieving this balance — along with adequate stability in physiological conditions — is a central challenge in AMP development.
The sequence space for AMPs is vast. Computational methods that can predict membrane disruption potential, selectivity indices, and stability profiles allow more efficient exploration of this space than experimental screening alone.
Our AMP Design Approach
Sequence-activity modeling
Machine learning models trained on AMP activity data predict potency and selectivity for candidate sequences.
Membrane interaction analysis
Computational analysis of membrane disruption mechanisms — pore formation, carpet model, toroidal pore — guides sequence design.
Selectivity optimization
Physicochemical property optimization (charge, hydrophobicity, amphipathicity) to maximize therapeutic index.
Stability engineering
Protease resistance and stability improvements through D-amino acid substitution, cyclization, and backbone modification strategies.
AMP Discovery Workflow
Target pathogen specification
Define the target organism(s), resistance profile, and clinical context.
Computational candidate generation
AI-assisted generation of candidate AMP sequences optimized for activity and selectivity.
Property prediction
Prediction of MIC, hemolytic activity, stability, and physicochemical properties for candidate ranking.
Candidate prioritization
Selection of priority candidates for experimental validation based on predicted profiles.
Optimization cycles
Iterative refinement of candidates based on experimental feedback.
Development candidate selection
Final candidate selection with stability and formulation assessment.
What Can Be Evaluated
What You May Receive
AMP candidate library
Ranked candidate sequences with predicted activity, selectivity, and stability profiles.
Mechanism analysis report
Computational analysis of membrane interaction mechanisms for priority candidates.
Optimization strategy
Recommended modifications to improve potency, selectivity, or stability.
Development roadmap
Path from initial candidates to a development-ready AMP.
Deliverables depend on project scope and target pathogen characteristics. Antimicrobial activity cannot be guaranteed.
Who This Is For
Infectious disease researchers
Academic groups studying AMR mechanisms and seeking novel AMP scaffolds for research or therapeutic development.
Biotech companies
Companies developing AMP-based therapeutics for drug-resistant bacterial, fungal, or biofilm infections.
Pharmaceutical teams
Drug discovery teams exploring peptide-based anti-infectives as alternatives to conventional antibiotics.
Screen Your AMP Sequence First
Before submitting a full AMP design project, use our free tools to assess your candidate sequence. The Drug-Likeness Predictor estimates hemolysis risk using the Eisenberg 1984 hydrophobic moment model — a key selectivity indicator for AMPs.
Frequently Asked Questions
Design Antimicrobial Peptides for Drug-Resistant Pathogens
Submit your target pathogen and project objectives. We will assess the AMP design opportunity and outline a computational discovery approach.
Confidential project discussions available under NDA.