SpectralTAG: Functional group identification from infrared spectra via unified LLM and GNN textual graphs.

BACKGROUND: Spectral analysis underpins much of modern chemical characterization, yet extracting chemically meaningful interpretations from spectral signals remains constrained by the disconnect between data-driven pattern recognition and structured domain knowledge.

RESULTS: Here we present SpectralTAG, a unified graph-based reasoning framework that bridges this gap by representing infrared spectra, hierarchical chemical knowledge graphs, and scientific text within a common textual attribute graph (TAG). In this representation, continuous spectra are discretized into semantically annotated range nodes, aligned with functional-group and vibration-type entities from a chemical ontology, and augmented with task-specific prompt and response nodes, enabling a single graph backbone to support node-level functional group classification, edge-level peak-pair relationship judgment, and graph-level molecular classification tasks through large language model encoding and graph neural network inference, without task-specific architectural redesign. Evaluated on 306 organic molecules with infrared spectra, SpectralTAG achieves AUC > 0.98 on edge- and graph-level tasks across 24 encoder-GNN combinations, with LLaMa2-7b paired with R-GCN delivering the most consistent cross-task performance. The framework further supports multitask training with negligible performance trade-off and exhibits emerging zero-shot and few-shot generalization under disjoint label conditions.

SIGNIFICANCE: These results establish textual attribute graphs as a viable paradigm for unifying heterogeneous scientific data within a single reasoning architecture, with implications beyond spectroscopy for broader knowledge-enhanced scientific analysis.