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arXiv · 2602.03875

Reversible Deep Learning for 13C NMR in Chemoinformatics: On Structures and Spectra

Stefan Kuhn·Vandana Dwarka·Przemyslaw Karol Grenda·Eero Vainikko
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Abstract

We introduce a reversible deep learning model for 13C NMR that uses a single conditional invertible neural network for both directions between molecular structures and spectra. The network is built from i-RevNet style bijective blocks, so the forward map and its inverse are available by construction. We train the model to predict a 128-bit binned spectrum code from a graph-based structure encoding, while the remaining latent dimensions capture residual variability. At inference time, we invert the same trained network to generate structure candidates from a spectrum code, which explicitly represents the one-to-many nature of spectrum-to-structure inference. On a filtered subset, the model is numerically invertible on trained examples, achieves spectrum-code prediction above chance, and produces coarse but meaningful structural signals when inverted on validation spectra. These results demonstrate that invertible architectures can unify spectrum prediction and uncertainty-aware candidate generation within one end-to-end model.

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Stefan Kuhn, Vandana Dwarka, Przemyslaw Karol Grenda, Eero Vainikko. 2026-04-23. Reversible Deep Learning for 13C NMR in Chemoinformatics: On Structures and Spectra. https://arxiv.org/abs/2602.03875

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