Initializing quantum engine
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QubitONeuron is building a research-backed computational engine combining quantum-inspired optimization, BIQML, physics-guided modelling, and antimicrobial peptide discovery to fight drug-resistant infections.
Watch the QubitONeuron founder pitch and learn how we are building a quantum-inspired AI engine for property-aware peptide therapeutics and antimicrobial resistance research.
Our work is inspired by recent advances in antimicrobial peptides, charge-density based machine learning, and quantum-inspired artificial intelligence. All referenced research and underlying methodologies are developed and authored by our founding team.
Directly inspires our charge-density based AMP framework by showing how electrostatic regimes reveal different molecular mechanisms behind antimicrobial peptide activity.
Introduces our Brain-Inspired Quantum Machine Learning architecture, showing improved robustness and cross-domain generalization through spiking dynamics and quantum-inspired probabilistic feedback.
Provides broader scientific support for using AI/ML and generative models to predict and design protein or peptide properties from biological sequence–structure information.
BIQML combines neural learning behaviour with quantum-inspired search to identify meaningful patterns in complex biological data.
Sequence, charge, hydrophobicity, stability, and physicochemical descriptors are collected.
Charge-density and biological constraints are encoded to reduce blind black-box prediction.
The system explores high-dimensional feature combinations more efficiently.
AMP candidates are ranked based on predicted activity, toxicity, stability, and biological relevance.
Antimicrobial resistance is one of the most urgent global health challenges, demanding faster and smarter therapeutic discovery.
AMR caused an estimated 1.27 million direct deaths globally in 2019.
India is among the high-burden regions where new antimicrobial strategies are urgently needed.
Our discovery engine converts biological data into optimized antimicrobial peptide candidates through a structured AI + quantum-inspired pipeline.
AMP and non-AMP peptide datasets.
Sequence, charge, structure, and physicochemical properties.
Low, mid, and high charge-density regimes.
Best feature combinations and peptide search.
Activity, toxicity, stability, and developability.
Most promising candidates selected.
Future lab feedback improves the model.
A cross-disciplinary founding team combining physics, artificial intelligence, quantum algorithms, and computational drug discovery.
AI/ML researcher in physics leading vision, product strategy, and quantum-inspired AI development.
Scientist in data-driven drug discovery leading AMP research, biology strategy, and validation roadmap.
Researcher in quantum computing algorithms and quantum AI leading technical architecture.
We are seeking early-stage funding, research partnerships, and computational resources to scale our platform, validate first AMP candidates, and build a defensible IP portfolio.
We are open to investors, pharma partners, biotech collaborators, research groups, and deeptech ecosystem supporters.