Quantum Protein Folding
Revolutionizing Protein Structure Prediction
with Quantum-First Precision
- Built To Order
- Production Ready
Understanding how proteins fold is fundamental to biology, medicine, and materials science. Traditional deep learning-based models attempt to predict protein structures but are limited to around 66% accuracy and rely on training data and statistical assumptions.
Automatski redefines the problem through a first-principles, ab-initio approach using quantum computation.
Predicting protein structure from a FASTA sequence is a computationally intensive challenge. In nature, proteins fold spontaneously in ~1 millisecond by achieving the lowest possible energy configuration. But simulating this behavior computationally is extremely difficult—there are astronomically many configurations a protein could theoretically fold into.
Identifying the globally lowest energy configuration among these requires exceptional computational capability.
Most quantum approaches to protein folding are limited to approximations using 3D lattice models, due to the complexity of representing and manipulating 3D spatial configurations on quantum systems.
Automatski overcomes this by using:
- A production-grade Eigen Solver
- A Universal Optimization Solver
- Quantum-first architecture to simulate folding with 99%+ accuracy directly from FASTA sequences
This method makes absolutely no assumptions about the final folded state—it calculates structure entirely from physical principles.
This unlocks transformative applications in:
- Drug discovery
- Synthetic biology
- Disease modeling
- Protein design
And it enables real-time folding simulation that was previously out of reach even for the most advanced classical systems.