Robust small-molecule identification from incomplete, degraded, and inconsistent spectra using multimodal mixed-condition training

2026-09-24T12:12:29.144Z

• title: Multimodal deep learning from spectra for small-molecule structure identification: enhancing robustness with mixed-condition training → Robust small-molecule identification from incomplete, degraded, and inconsistent spectra using multimodal mixed-condition training
• summary: 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, qualit → Reliable small-molecule identification often requires complementary evidence from multiple spectroscopic measurements. In practice, however, spectra may be unavailable, degraded by measurement-related variations, or even incorrectly associated with a sample, thereby hindering accurate molecular identification. Herein, we propose a multimodal mixed-condition training strategy that accommodates missing, degraded, and mismatched measurements for small-molecule structure identification. The strategy incorporates chemical and spectroscopic knowledge through predefined missing-input configurations,