QOBRA: A Quantum Operator-Based Autoencoder for De Novo Molecular Design

2025/07/18 by Yue Yu, Francesco Calcagno, Haote Li +1 · 1 voice
Materials Science · Computer Science · Physics and Astronomy · #Machine Learning in Materials Science #Quantum Computing Algorithms and Architecture #Quantum many-body systems

paper · pdf · doi:10.1101/2025.07.15.665010

openalex publication_date 2025/07/18 · openalex created_date 2025/11/02 · openalex updated_date 2026/07/15

Abstract

We introduce a variational quantum autoencoder tailored for de novo molecular design named QOBRA (Quantum Operator-Based Real-Amplitude autoencoder). QOBRA le circuits for real-amplitude encoding and the SWAP test to estimate reconstruction and latent-space regularization errors during back-propagation. Adjoint encoderand decoder unitary transformations and a generative process that ensures accurate reconstruction as well as novelty, uniqueness, and validity of the generated samples. We showcase QOBRA as applied to de novo design of Ca 2+ -, Mg 2+ -, and Zn 2+ -binding metalloproteins after training the generative model with a modest dataset. Significance Statement Recent advancements in classical generative machine learning have shown significant strides in molecular design for targeted applications. N onetheless, these advancements are fundamentally limited by classical computation based on binary units. We introduce a quantum computation-based ML framework employing qubits, which exhibits the ability to synthesize de novo molecular instances with specified properties from limited datasets. Quantum networks require exponentially fewer parameters than classical ones, enhancing their trainability and efficiency. While our demonstration focuses on metalloprotein primary sequences, the paradigm is adaptable to diverse molecular designs. This integration of AI and quantum computing holds potential to expand the scientific and technological frontiers of both domains within a practical framework.

Citations

• Stabilization of Quantum Computations by Symmetrization
• Robust deep learning–based protein sequence design using ProteinMPNN
• Benchmarking AlphaFold2 on peptide structure prediction
• DSSP 4: FAIR annotation of protein secondary structure
• Quantum Machine Learning in Drug Discovery: Applications in Academia and Pharmaceutical Industries
• Training quantum machine learning models on cloud without uploading the data
• Accurate structure prediction of biomolecular interactions with AlphaFold 3
• Quantum to Classical Neural Network Transfer Learning Applied to Drug Toxicity Prediction
• OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials
• Kernel-Elastic Autoencoder for Molecular Design
• De novo design of protein structure and function with RFdiffusion
• Evolutionary-scale prediction of atomic-level protein structure with a language model
• Efficient quantum tomography II
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Discussions

• Protein design with Quantum Machine Learning #QML. Not sci-fi, but reality! Read more about QOBRA in our latest work 👇 doi.org/10.1101/2025... [bsky, 1 points, 0 comments]

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