Multimodal deep learning from spectra for small-molecule structure identification: enhancing robustness with mixed-condition training

תקציר מקורי באנגליתarXiv:2609.14360v1 Announce Type: new Abstract: In practical molecular characterization, small-molecule structure identification benefits from complementary spectroscopic evidence, but missing, degraded, or mismatched spectra challenge multimodal models. Herein, we incorporate domain knowledge from spectroscopy and chemistry into mixed-condition training for candidate structure reranking, using a reproducible evaluation protocol and mixture-of-experts (MoE) fusion. The protocol incorporates perturbations tailored to each spectroscopic modality and chemically informed spectrum replacements to cover variations in spectral availability, quality, and consistency. A total of 79,462 test samples were evaluated across 30 predefined conditions using simulated spectra from the Multimodal Spectrosco