Nuclear Magnetic Resonance (NMR) spectroscopy is the gold standard for molecular structure elucidation, yet interpreting complex spectra for unknown molecules remains a bottleneck reliant on human expertise. While artificial intelligence has advanced this field, current methods face a critical trade-off: database retrieval cannot identify novel scaffolds, while de novo molecular structure elucidation models operate as black boxes, lacking the atom-level interpretability required for rigorous scientific validation. Here, we present NMRAgent, an evidential reasoning agent powered by large language models (LLMs) that bridges this gap by integrating specialized spectral analysis tools with chemical knowledge graphs. Unlike previous approaches, NMRAgent mimics the deductive reasoning of human experts: it takes experimental NMR spectra and molecular formula as input, plans the elucidation process, proposes candidate structures, verifies peak-atom consistency, and refines misaligned substructure through formula-aware fragment optimization. Enabled by its evidential reasoning, NMRAgent outperforms state-of-the-art methods, improving top-1 accuracy by 46.5% and Tanimoto similarity by 0.502 on a scaffold-split benchmark with novel scaffolds in the test set. Besides, we demonstrate the agent's practical utility by elucidating the structures of two previously unknown natural products isolated from Hydrangea davidii and Vitex trifolia, and by correcting structural misassignments in established literature. By combining high-accuracy prediction with transparent and evidence-based reasoning, NMRAgent establishes a new paradigm for interpretable AI in analytical chemistry.
Zheng Fang, Yang Chen, Yusen Tan et al.· arXiv.org· 0 citations
X-ray absorption spectroscopy (XAS) is a key technique for probing local atomic environments, yet learning based modeling must bridge two heterogeneous modalities: 1D continuous spectra and 3D atomic structures. Existing approaches typically decouple forward spectrum prediction and inverse structure inference into separate regression tasks, hindering shared representation learning. Moreover, severe permutation ambiguity among identical atoms often limits inverse modeling to coarse structure descriptors rather than explicit 3D structure generation. In this work, we present Uni-XAS, a unified benchmark and learning framework that reframes bidirectional XAS modeling as a cross-modal alignment and conditional generation problem. We first propose XASLip, an alignment recipe coupling a physics-aware spectral encoder with an absorberaware manifold optimization strategy to resolve fine-grained intra-element coordination variations. Building upon this shared latent space, we formulate forward prediction as anchored absolute-spectrum generation via retrieval-augmented decoding, effectively preventing physical scale collapse and energy drift. For the inherently ill-posed inverse problem, we introduce Permutation-Rectified Flow Matching, which integrates type-wise optimal transport into a continuous generative flow to provide a principled solution to ligand permutation ambiguity without relying on heavy high-order equivariant architectures. Evaluated on a largescale standardized benchmark of 328,839 structure-spectrum pairs, Uni-XAS demonstrates strong performance in cross-modal retrieval, accurate absolute-spectrum prediction, and composition-conditional 3D structure generation, establishing a scalable, reproducible, and protocol-consistent foundation for multimodal learning and standardized evaluation in scientific spectroscopy.
Suyang Zhong, Yuhao Zhao, Boying Huang et al.· 0 citations