Unraveling Molecular Structure: A Multimodal Spectroscopic Dataset for Chemistry

• URL: http://arxiv.org/abs/2407.17492v2
• Date: Tue, 29 Oct 2024 15:54:13 GMT
• Title: Unraveling Molecular Structure: A Multimodal Spectroscopic Dataset for Chemistry
• Authors: Marvin Alberts, Oliver Schilter, Federico Zipoli, Nina Hartrampf, Teodoro Laino,
• Abstract summary: 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.
• Score: 0.1747623282473278
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Spectroscopic techniques are essential tools for determining the structure of molecules. Different spectroscopic techniques, such as Nuclear magnetic resonance (NMR), Infrared spectroscopy, and Mass Spectrometry, provide insight into the molecular structure, including the presence or absence of functional groups. Chemists leverage the complementary nature of the different methods to their advantage. However, the lack of a comprehensive multimodal dataset, containing spectra from a variety of spectroscopic techniques, has limited machine-learning approaches mostly to single-modality tasks for predicting molecular structures from spectra. Here we introduce a dataset comprising simulated $^1$H-NMR, $^{13}$C-NMR, HSQC-NMR, Infrared, and Mass spectra (positive and negative ion modes) for 790k molecules extracted from chemical reactions in patent data. This dataset enables the development of foundation models for integrating information from multiple spectroscopic modalities, emulating the approach employed by human experts. Additionally, we provide benchmarks for evaluating single-modality tasks such as structure elucidation, predicting the spectra for a target molecule, and functional group predictions. This dataset has the potential automate structure elucidation, streamlining the molecular discovery pipeline from synthesis to structure determination. The dataset and code for the benchmarks can be found at https://rxn4chemistry.github.io/multimodal-spectroscopic-dataset.

Related papers

• Infrared Spectra Prediction for Diazo Groups Utilizing a Machine Learning Approach with Structural Attention Mechanism [0.0]
  Infrared (IR) spectroscopy is a pivotal technique in chemical research for elucidating molecular structures and dynamics through vibrational and rotational transitions. Here, we present a machine learning approach employing a Structural Attention Mechanism tailored to enhance the prediction and interpretation of infrared spectra, particularly for diazo compounds.
  arXiv  Detail & Related papers  (2024-02-05T15:44:43Z)
• Datacube segmentation via Deep Spectral Clustering [76.48544221010424]
  Extended Vision techniques often pose a challenge in their interpretation. The huge dimensionality of data cube spectra poses a complex task in its statistical interpretation. In this paper, we explore the possibility of applying unsupervised clustering methods in encoded space. A statistical dimensional reduction is performed by an ad hoc trained (Variational) AutoEncoder, while the clustering process is performed by a (learnable) iterative K-Means clustering algorithm.
  arXiv  Detail & Related papers  (2024-01-31T09:31:28Z)
• Carbohydrate NMR chemical shift predictions using E(3) equivariant graph neural networks [0.0]
  This work introduces a novel approach that leverages E(3) equivariant graph neural networks to predict carbohydrate NMR spectra. Notably, our model achieves a substantial reduction in mean absolute error, up to threefold, compared to traditional models. The implications are far-reaching and go beyond an advanced understanding of carbohydrate structures and spectral interpretation.
  arXiv  Detail & Related papers  (2023-11-21T15:01:14Z)
• Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning [60.02391969049972]
  We introduce a novel deep learning framework, called Distributional Graphormer (DiG), in an attempt to predict the equilibrium distribution of molecular systems. DiG employs deep neural networks to transform a simple distribution towards the equilibrium distribution, conditioned on a descriptor of a molecular system.
  arXiv  Detail & Related papers  (2023-06-08T17:12:08Z)
• Prefix-Tree Decoding for Predicting Mass Spectra from Molecules [12.868704267691125]
  We use a new intermediate strategy for predicting mass spectra from molecules by treating mass spectra as sets of molecular formulae, which are themselves multisets of atoms. We show promising empirical results on mass spectra prediction tasks.
  arXiv  Detail & Related papers  (2023-03-11T17:44:28Z)
• 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)
• Gaussian Process Regression for Absorption Spectra Analysis of Molecular Dimers [68.8204255655161]
  We discuss an approach based on a machine learning technique, where the parameters for the numerical calculations are chosen from Gaussian Process Regression (GPR) This approach does not only quickly converge to an optimal parameter set, but in addition provides information about the complete parameter space. We find that indeed the GPR gives reliable results which are in agreement with direct calculations of these parameters using quantum chemical methods.
  arXiv  Detail & Related papers  (2021-12-14T17:46:45Z)
• 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)
• Machine Learning for recognition of minerals from multispectral data [1.231476564107544]
  We present novel methods for automatic mineral identification based on combining data from different spectroscopic methods. These methods were paired into Raman + VNIR, Raman + LIBS and VNIR + LIBS, and different methods of data fusion applied to each pair to classify minerals. We also present a Deep Learning algorithm for mineral classification from Raman spectra that outperforms previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-05-28T22:25:15Z)
• Two-Dimensional Single- and Multiple-Quantum Correlation Spectroscopy in Zero-Field Nuclear Magnetic Resonance [55.41644538483948]
  We present single- and multiple-quantum correlation $J$-spectroscopy detected in zero magnetic field using a Rb vapor-cell magnetometer. At zero field the spectrum of ethanol appears as a mixture of carbon isotopomers, and correlation spectroscopy is useful in separating the two composite spectra.
  arXiv  Detail & Related papers  (2020-04-09T10:02:45Z)

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.