用可逆神经网络统一预测分子结构与13C NMR谱图，支持双向推理。
Reversible Deep Learning for 13C NMR in Chemoinformatics: On Structures and Spectra
- 采用可逆神经网络，实现结构到谱图与谱图到结构的双向映射。
- 在过滤数据集上，谱图编码预测准确率高于随机水平，反向生成结构具合理性。
- 适合需不确定性的分子结构推断任务，如新化合物发现。
我们提出一种用于13C NMR的可逆深度学习模型，使用单一条件可逆神经网络实现分子结构与谱图之间的双向映射。网络基于i-RevNet风格的双射块构建，正向与逆向映射天然可用。模型通过图结构编码预测128位分箱谱图码，其余潜在维度捕捉残差变异。推理时，利用同一训练网络反向生成结构候选，显式体现谱图到结构推理中的一对多特性。在过滤子集上，模型在训练样本上具备数值可逆性，谱图码预测优于随机水平，并在验证谱图反演时产生粗粒度但有意义的结构信号。结果表明，可逆架构可在单个端到端模型中统一谱图预测与不确定性感知的结构生成。
原文摘要 · Abstract (English)
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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