SpecTUS: Spectral Translator for Unknown Structures annotation from EI-MS spectra

• URL: http://arxiv.org/abs/2502.05114v1
• Date: Fri, 07 Feb 2025 17:36:13 GMT
• Title: SpecTUS: Spectral Translator for Unknown Structures annotation from EI-MS spectra
• Authors: Adam Hájek, Helge Hecht, Elliott J. Price, Aleš Křenek,
• Abstract summary: We propose SpecTUS: Spectral Translator for Unknown Structures, a deep neural model that addresses the task of structural annotation of small molecules. Our model analyzes the spectra in structuralittextde novo manner -- a direct translation from the spectra into 2D representation. In a rigorous evaluation of our model on the novel structure annotation task across different libraries, we outperformed standard database search techniques by a wide margin.
• Score: 0.0
• License:
• Abstract: Compound identification and structure annotation from mass spectra is a well-established task widely applied in drug detection, criminal forensics, small molecule biomarker discovery and chemical engineering. We propose SpecTUS: Spectral Translator for Unknown Structures, a deep neural model that addresses the task of structural annotation of small molecules from low-resolution gas chromatography electron ionization mass spectra (GC-EI-MS). Our model analyzes the spectra in \textit{de novo} manner -- a direct translation from the spectra into 2D-structural representation. Our approach is particularly useful for analyzing compounds unavailable in spectral libraries. In a rigorous evaluation of our model on the novel structure annotation task across different libraries, we outperformed standard database search techniques by a wide margin. On a held-out testing set, including \numprint{28267} spectra from the NIST database, we show that our model's single suggestion perfectly reconstructs 43\% of the subset's compounds. This single suggestion is strictly better than the candidate of the database hybrid search (common method among practitioners) in 76\% of cases. In a~still affordable scenario of~10 suggestions, perfect reconstruction is achieved in 65\%, and 84\% are better than the hybrid search.

Related papers

• DiffMS: Diffusion Generation of Molecules Conditioned on Mass Spectra [60.39311767532607]
  DiffMS is a formula-restricted encoder-decoder generative network. We develop a robust decoder that bridges latent embeddings and molecular structures. Experiments show DiffMS outperforms existing models on $textitde novo$ molecule generation.
  arXiv  Detail & Related papers  (2025-02-13T18:29:48Z)
• MADGEN: Mass-Spec attends to De Novo Molecular generation [16.89017809745962]
  We propose a scaffold-based method for de novo molecular structure generation guided by mass spectrometry data. MADGEN operates in two stages: scaffold retrieval and spectra-conditioned molecular generation. We evaluate MADGEN on three datasets (NIST23, CANOPUS, and MassSpecGym)
  arXiv  Detail & Related papers  (2025-01-03T18:54:26Z)
• JESTR: Joint Embedding Space Technique for Ranking Candidate Molecules for the Annotation of Untargeted Metabolomics Data [8.964879518873591]
  We introduce a novel paradigm (JESTR) for annotation. Unlike prior approaches that explicitly construct molecular fingerprints or spectra, JESTR embeds their representations in a joint space. We evaluate JESTR against mol-to-spec and spec-to-FP annotation tools on three datasets.
  arXiv  Detail & Related papers  (2024-11-18T03:03:57Z)
• Deep Spectral Methods for Unsupervised Ultrasound Image Interpretation [53.37499744840018]
  This paper proposes a novel unsupervised deep learning strategy tailored to ultrasound to obtain easily interpretable tissue separations. We integrate key concepts from unsupervised deep spectral methods, which combine spectral graph theory with deep learning methods. We utilize self-supervised transformer features for spectral clustering to generate meaningful segments based on ultrasound-specific metrics and shape and positional priors, ensuring semantic consistency across the dataset.
  arXiv  Detail & Related papers  (2024-08-04T14:30:14Z)
• Unraveling Molecular Structure: A Multimodal Spectroscopic Dataset for Chemistry [0.1747623282473278]
  This dataset comprises simulated $1$H-NMR, $13$C-NMR, HSQC-NMR, Infrared, and Mass spectra for 790k molecules extracted from chemical reactions in patent data. We provide benchmarks for evaluating single-modality tasks such as structure elucidation, predicting the spectra for a target molecule, and functional group predictions.
  arXiv  Detail & Related papers  (2024-07-04T12:52:48Z)
• Mass Spectra Prediction with Structural Motif-based Graph Neural Networks [21.71309513265843]
  MoMS-Net is a system that predicts mass spectra using the information derived from structural motifs and the implementation of Graph Neural Networks (GNNs) We have tested our model across diverse mass spectra and have observed its superiority over other existing models.
  arXiv  Detail & Related papers  (2023-06-28T10:33:57Z)
• Decoding Structure-Spectrum Relationships with Physically Organized Latent Spaces [6.36075035468233]
  A new semi-supervised machine learning method for the discovery of structure-spectrum relationships is developed and demonstrated. This method constructs a one-to-one mapping between individual structure descriptors and spectral trends. The RankAAE methodology produces a continuous and interpretable latent space, where each dimension can track an individual structure descriptor.
  arXiv  Detail & Related papers  (2023-01-11T21:30:22Z)
• Unsupervised Machine Learning for Exploratory Data Analysis of Exoplanet Transmission Spectra [68.8204255655161]
  We focus on unsupervised techniques for analyzing spectral data from transiting exoplanets. We show that there is a high degree of correlation in the spectral data, which calls for appropriate low-dimensional representations. We uncover interesting structures in the principal component basis, namely, well-defined branches corresponding to different chemical regimes.
  arXiv  Detail & Related papers  (2022-01-07T22:26:33Z)
• Unsupervised Spectral Unmixing For Telluric Correction Using A Neural Network Autoencoder [58.720142291102135]
  We present a neural network autoencoder approach for extracting a telluric transmission spectrum from a large set of high-precision observed solar spectra from the HARPS-N radial velocity spectrograph.
  arXiv  Detail & Related papers  (2021-11-17T12:54:48Z)
• Spectral Analysis Network for Deep Representation Learning and Image Clustering [53.415803942270685]
  This paper proposes a new network structure for unsupervised deep representation learning based on spectral analysis. It can identify the local similarities among images in patch level and thus more robust against occlusion. It can learn more clustering-friendly representations and is capable to reveal the deep correlations among data samples.
  arXiv  Detail & Related papers  (2020-09-11T05:07:15Z)
• A Systematic Approach to Featurization for Cancer Drug Sensitivity Predictions with Deep Learning [49.86828302591469]
  We train >35,000 neural network models, sweeping over common featurization techniques. We found the RNA-seq to be highly redundant and informative even with subsets larger than 128 features.
  arXiv  Detail & Related papers  (2020-04-30T20:42:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.