Pharmaceutical & Healthcare
Quantum computing accelerates drug discovery and biomedical research by solving molecular optimization problems that are computationally infeasible classically — protein folding, RNA structure prediction, and molecular screening.Protein Folding
Predicting the three-dimensional structure of a protein from its amino acid sequence is one of biology’s hardest problems. The folding path can be encoded as a QUBO and solved on Dynex to find low-energy conformations.Quantum Protein Folding
Lattice protein folding via quantum annealing. Finds minimal-energy conformations for peptide chains on a 2D/3D lattice.
RNA Folding
RNA secondary structure prediction — finding the minimum free energy fold — maps naturally to a QUBO. This example folds the Tobacco Mild Green Mosaic Virus RNA sequence on Dynex.Quantum RNA Folding
Minimum free energy RNA folding of the TMGMV sequence. Based on Fox et al., PLoS Comput Biol (2022).
Molecule Screening
Virtual screening of phenol derivatives to identify candidates with desired physicochemical properties. Group contribution methods are combined with a QUBO formulation to efficiently explore large chemical spaces.Efficient Exploration of Phenol Derivatives
QUBO-based molecular screening using group contribution approaches. Identifies optimal phenol derivative candidates for industrial applications.
Enzyme Target Prediction
Quantum optimization applied to enzyme-target identification — a key step in drug discovery pipelines. Formulates the binding problem as a QUBO to identify enzyme targets efficiently.Enzyme Target Prediction
QuTIE: Quantum optimization for Target Identification by Enzymes.
Breast Cancer Prediction
Quantum feature selection for breast cancer classification using the Dynex scikit-learn plugin. Formulates mutual information-based feature selection as a QUBO problem.Breast Cancer Prediction (scikit-learn Plugin)
Quantum feature selection applied to the Wisconsin Breast Cancer Dataset using the Dynex scikit-learn transformer.