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

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 Spectroscopic Dataset (MSSD) for mass spectrometry (MS), infrared (IR) spectroscopy, and 1H and 13C nuclear magnetic resonance (NMR), with up to 128 hard candidate structures per sample. A controlled two-by-two factorial comparison of complete-input training versus mixed-condition training and vanilla concatenation versus MoE fusion, with matched evaluation conditions, showed that mixed-condition training provided the main gains in both architectures. For MoE, mean reciprocal rank (MRR), averaged equally across conditions, increased from 0.9203 to 0.9763, a relative increase of 6.08%. Recall at rank 1 (R@1), averaged over the same conditions, increased from 89.50% to 96.36%, an increase of 6.86 percentage points and a relative increase of 7.67%. IR-only and MS/MS-only MRR increased from 0.4337 to 0.9307 and from 0.3711 to 0.8575, reaching 2.15 and 2.31 times their respective baseline values, while complete-input performance remained high. These results support integrating domain knowledge into training-condition design to improve robustness, with further gains from MoE under mixed-condition training.