Persistent Homology with Vineyards for Robust Spectral Representation

Automated molecular structure elucidation from experimental spectra is fundamentally challenged by noise, baseline distortions, overlapping signals, and modality-specific artifacts. Existing neural encoders typically represent spectra either as dense intensity grids, entangling experimental artifacts with chemical information, or as sparse peak lists that depend on heuristic detection thresholds. We introduce VINEA, a modality-general framework that represents spectra through their multiscale topological structure, capturing persistent spectral features while suppressing transient noise and preserving chemically meaningful relationships. VINEA provides a unified representation across 1D NMR, HSQC, IR, and Raman spectra. With approximately 1M parameters, VINEA provides the strongest overall robustness on ${}^1$H NMR and HSQC retrieval, leads Raman robustness when trained on clean spectra, and leads IR functional-group prediction under corruption-aware training. After fine-tuning on experimental spectra, VINEA matches or exceeds the strongest specialized architectures on NMR, HSQC, and Raman while remaining competitive on IR. These results establish spectral topology as a compact, robust, and modality-agnostic representation for automated structure elucidation.