Multi-Modal Contrastive Learning for Chemical Structure Elucidation with VibraCLIP

Multi-modal contrastive learning offers a powerful framework for molecular structure elucidation from vibrational spectra. VibraCLIP applies this approach by embedding infrared (IR), Raman spectra, and molecular graphs into a unified latent space, enabling accurate, on-the-fly, and scalable molecular identification. Aligning IR and Raman modalities boosts Top-1 retrieval accuracy from 12.4% to 62.9%, while incorporating standardized molecular mass elevates Top-25 accuracy to 98.9%, underscoring the value of chemically grounded anchoring features. VibraCLIP’s three-way alignment strategy captures the complementary nature of vibrational modes, offering substantial improvements over single-modality baselines. A lightweight fine-tuning protocol, updating only the final projection layer, enables robust generalization from theoretical to experimental datasets. This flexible, data-efficient framework transforms vibrational spectroscopy into a high-precision tool for molecular discovery. VibraCLIP establishes a new standard for AI-driven spectral interpretation, bridging molecular structure and spectroscopy with broad impact in fields as separated as drug discovery or astrochemical observations.