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

We introduce a variational quantum autoencoder tailored for de novo molecular design, named <b>QO-BRA</b> (Quantum Operator-Based Real Amplitude autoencoder). QO-BRA leverages quantum circuits for real-amplitude encoding and the SWAP test to estimate reconstruction and latent-space regularization errors during back-propagation. Adjoint encoder and decoder operators enable unitary transformations and a generative process that ensures accurate reconstruction, as well as the novelty, uniqueness, and validity of the generated samples. We showcase the capabilities of QO-BRA as applied to the de novo design of Ca<sup>2+</sup>-, Mg<sup>2+</sup>-, and Zn<sup>2+</sup>-binding metalloproteins after training the generative model with a modest data set.

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Publication Details

Journal
Journal of Chemical Theory and Computation
Published
2026-02-03
DOI
https://doi.org/10.1021/acs.jctc.5c01704
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
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article

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

Francesco Calcagno, Yue Yu, Haote Li, Victor S. Batista
Journal of Chemical Theory and Computation
Machine Learning in Materials Science
article

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

Francesco Calcagno, Yue Yu, Haote Li, Victor S. Batista
article en

Abstract

We introduce a variational quantum autoencoder tailored for de novo molecular design, named <b>QO-BRA</b> (Quantum Operator-Based Real Amplitude autoencoder). QO-BRA leverages quantum circuits for real-amplitude encoding and the SWAP test to estimate reconstruction and latent-space regularization errors during back-propagation. Adjoint encoder and decoder operators enable unitary transformations and a generative process that ensures accurate reconstruction, as well as the novelty, uniqueness, and validity of the generated samples. We showcase the capabilities of QO-BRA as applied to the de novo design of Ca<sup>2+</sup>-, Mg<sup>2+</sup>-, and Zn<sup>2+</sup>-binding metalloproteins after training the generative model with a modest data set.

Journal of Chemical Theory and ComputationVol. 22(4)
Yale University (US), Division of Undergraduate Education (US), University of Bologna (IT)
Openalex Percentile: Top 69%
Machine Learning in Materials Science
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QO-BRA: A Quantum Operator-Based Autoencoder for De Novo Molecular Design — Francesco Calcagno, Yue Yu, et al. · Journal of Chemical Theory and Computation (2026) | TGRS Research Map | TGRS