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.
Authors
- Francesco Calcagno (ORCID: https://orcid.org/0000-0002-0986-4425)
- Yue Yu (ORCID: https://orcid.org/0000-0002-5509-2235)
- Haote Li (ORCID: https://orcid.org/0000-0002-8146-5066)
- Victor S. Batista
Institutions
- Yale University (US)
- Division of Undergraduate Education (US)
- University of Bologna (IT)
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
- 0.00