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  3. Spectre: A Multimodal Spectral Transformer For Small Molecule Annotation.
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  3. Spectre: A Multimodal Spectral Transformer For Small Molecule Annotation.

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SPECTRE: A Multimodal Spectral Transformer for Small Molecule Annotation.

Wangdong Xu1, Byeol Ryu2, Anthony Tong1

  • 1Department of Computer Science and Engineering, University of California San Diego, La Jolla, California 92093, United States.

Journal of Chemical Information and Modeling
|February 25, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

SPECTRE, a new AI model, accelerates natural product drug discovery by automating the interpretation of Nuclear Magnetic Resonance (NMR) spectra. This tool enhances structure annotation and retrieval accuracy for pharmaceutical research.

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Area of Science:

  • Chemistry
  • Computational Biology
  • Drug Discovery

Background:

  • Natural products (NPs) are crucial for developing pharmaceuticals like penicillin and anticancer drugs.
  • Nuclear Magnetic Resonance (NMR) spectroscopy is vital for determining NP chemical structures.
  • Manual NMR spectra interpretation is time-consuming and requires specialized expertise.

Purpose of the Study:

  • To introduce SPECTRE, a transformer-based computational tool for accelerating NP structure elucidation.
  • To improve the accuracy and efficiency of structure dereplication and annotation from NMR data.
  • To provide interpretable insights for chemists, aiding hypothesis generation.

Main Methods:

  • Development of a novel transformer-based model for structure annotation using diverse NMR data.
  • Creation of an entropy-optimized, collision-free molecular binary fingerprint for enhanced candidate retrieval.
  • Implementation of fine-grained similarity maps for substructure-level interpretation.

    • Main Results:

    • SPECTRE achieves a state-of-the-art 80% top-1 annotation accuracy on a large dataset (526,163 molecules).
    • The tool provides the first fine-grained similarity maps, enabling substructure interpretation.
    • Enhanced accuracy in retrieving molecular candidates using the novel fingerprinting method.

    Conclusions:

    • SPECTRE significantly accelerates the structure elucidation process for natural products.
    • The model's interpretability features offer valuable guidance for chemists.
    • SPECTRE represents a significant advancement in computational tools for drug discovery and chemical analysis.