> ## Documentation Index
> Fetch the complete documentation index at: https://dynex.mintlify.app/llms.txt
> Use this file to discover all available pages before exploring further.

# Pharma & Health

> Quantum computing applications in drug discovery, protein folding, and biomedical research

# 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.

<Card title="Quantum Protein Folding" icon="dna" href="https://github.com/Dynex-Development/awesome-dynex/blob/main/advanced_applications/QuantumProteinFolding.ipynb">
  Lattice protein folding via quantum annealing. Finds minimal-energy conformations for peptide chains on a 2D/3D lattice.
</Card>

**Scientific background:** Irbäck et al. (2022). *Folding lattice proteins with quantum annealing.*

## 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.

<Card title="Quantum RNA Folding" icon="bacteria" href="https://github.com/Dynex-Development/awesome-dynex/blob/main/misc/example_rna_folding.ipynb">
  Minimum free energy RNA folding of the TMGMV sequence. Based on Fox et al., PLoS Comput Biol (2022).
</Card>

**Scientific background:** Fox DM et al. *RNA folding using quantum computers.* PLoS Comput Biol. 2022;18(4):e1010032.

## 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.

<Card title="Efficient Exploration of Phenol Derivatives" icon="flask-vial" href="https://github.com/Dynex-Development/awesome-dynex/blob/main/misc/molecule_screening.ipynb">
  QUBO-based molecular screening using group contribution approaches. Identifies optimal phenol derivative candidates for industrial applications.
</Card>

**Scientific background:** Cho et al. *Efficient Exploration of Phenol Derivatives Using QUBO Solvers.* Ind. Eng. Chem. Res. 2024, 63(10), 4248–4256.

## 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.

<Card title="Enzyme Target Prediction" icon="microscope" href="https://github.com/samgr55/Enzyme-TargetPrediction_QUBO-Ising">
  QuTIE: Quantum optimization for Target Identification by Enzymes.
</Card>

**Scientific background:** Ngo HM et al. *QuTIE: Quantum optimization for Target Identification by Enzymes.* Bioinformatics Advances, 2023.

## 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.

<Card title="Breast Cancer Prediction (scikit-learn Plugin)" icon="heart-pulse" href="https://github.com/Dynex-Development/awesome-dynex/blob/main/machine_learning/Dynex_Scikit-Learn_Plugin.ipynb">
  Quantum feature selection applied to the Wisconsin Breast Cancer Dataset using the Dynex scikit-learn transformer.
</Card>

**Scientific background:** Bhatia & Phillipson. *Performance Analysis of SVM Implementations on the D-Wave Quantum Annealer.* ICCS 2021.