Superspecies AI
AI-First Antimicrobial Discovery Engineered to Outpace Resistance
We design antibiotics, peptides, and biologics using genomics-aware deep learning, resistance-evolution simulations, and ADMET triage — delivering lab-ready candidates with transparent rationales.
- Predicted Hit Rate
- 88%
- Resistance Drop (sim.)
- 33×
- AMR Biomarkers Indexed
- 128M+
Target Intelligence
Mine genomes, metagenomes, and the resistome to surface novel bacterial vulnerabilities and priority targets.
Generative AMPs & Biologics
Design antimicrobial peptides, antibody fragments, and phage-derived payloads with transformer and graph neural models.
Resistance-Evolution Predictor
Simulate escape pathways (efflux, target mutation, enzymatic breakdown) to score “resistance resilience.”
ADMET + Microbiome
Predict PK/PD, tox, and collateral microbiome impact to prioritize leads that truly translate.
Why It Matters
Antimicrobial resistance (AMR) is projected to cause 10 million deaths annually by 2050. The discovery pipeline has slowed drastically — traditional screening and optimization methods can’t keep pace with mutational dynamics of pathogens. Superspecies AI redefines discovery with data-driven intelligence, focusing on adaptability, explainability, and clinically relevant outcomes.
Global Impact
Focus on WHO-critical pathogens and emerging resistance markers.
Speed
From target definition to in silico lead within weeks, not months.
Transparency
Explainable AI outputs with uncertainty bands and mode-of-action maps.
Discovery Pipeline
- Target & Phenotype Ingest →
- Generative Design →
- Docking & MD Screens →
- Resistance Simulation →
- ADMET Triage →
- Wet-Lab Handoff
Research & Collaborations
We collaborate across computational biology, structural chemistry, and clinical microbiology to validate our predictions in wet-lab environments. Our ongoing partnerships bridge AI with translational medicine.
Our AI Infrastructure
Built with high-performance computing pipelines and proprietary resistome datasets, our architecture integrates molecular generation (GraphNets, Transformers), binding prediction (Docking + AlphaFold2), and toxicity assessment (multi-task learning).
- • PyTorch Geometric, RDKit, and DeepChem
- • Distributed training with GPU clusters
- • Supabase + FastAPI for data pipelines
- • Secure sandbox for hospital AMR datasets
Visualization Example
A peptide library mapped across 200 resistance genotypes. Color depth represents mutation hotspots; vector density indicates model confidence.
Clinically Grounded, Partner-Ready
We integrate hospital isolate data and surveillance feeds to keep models aligned with real-world resistance. Outputs ship with assumptions, uncertainty bands, and experiment-ready protocols for CRO or academic labs.
“Superspecies AI surfaced peptide candidates with low predicted escape routes against NDM-1 producers. The simulation reports mapped likely mutations — we took two into wet-lab within a week.”
— Partner Lab, Pilot Study