Towards Generalizable and Evidential Nuclear Magnetic Resonance-Based Molecular Structure Elucidation via Large Language Model Agent
arXiv:2606.29776v1 Announce Type: new Abstract: 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 ev
The proliferation of advanced AI models, particularly large language models, provides the computational and inferential power to tackle complex scientific problems like molecular structure elucidation that previously relied on heuristic approaches and human expertise.
This breakthrough addresses a significant bottleneck in molecular discovery and drug development, offering a more efficient, accurate, and interpretable method for understanding molecular structures critical for various scientific and industrial applications.
The process of identifying novel molecular scaffolds will transition from a labor-intensive, expertise-dependent task to a more automated and AI-driven workflow, potentially accelerating research timelines and reducing costs in fields like pharmaceuticals and materials science.
- · Pharmaceutical industry
- · Material science companies
- · AI-driven drug discovery platforms
- · Spectroscopy instrument manufacturers
- · Traditional contract research organizations (CROs) specializing in NMR interpret
- · Manual structural elucidation service providers
More rapid identification and validation of new drug candidates and materials will occur.
The cost of molecular discovery could decrease, leading to a broader range of research and development projects.
This could enable the design and synthesis of currently unknown molecules with highly specific properties, opening new frontiers in medicine and engineering.
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