Unraveling Molecular Structure: A Multimodal Spectroscopic Dataset for Chemistry

TL;DR

A dataset comprising simulated spectra from a variety of spectroscopic techniques for 790k molecules extracted from chemical reactions in patent data that enables the development of foundation models for integrating information from multiple spectroscopic modalities, emulating the approach employed by human experts.

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.

Figures (14)

• Figure 1: Overall workflow: Molecules are extracted from reaction data (USPTO), filtered to only contain certain atom types as well as minimum and maximum molecule size, then for each molecule the corresponding spectra are simulated resulting in a dataset of spectra for 790k molecules.
• Figure 2: (A) Size and functional group (B) distribution of the full dataset. 200 randomly sampled molecules were investigated for their chemical similarity to each (C) as well as if the IR spectra similarity correlates to the chemical similarity (D).
• Figure 3: Simulated vs experimentally measured (A) $^1$H-NMR (B) $^{13}$C-NMR (C) MS/MS [+40 eV] and (D) IR of the molecule 2,2-dimethyl-3-methylenebicyclo[2.2.1]heptane (shown in the lower right).
• Figure 4: Overview of the benchmarks. Left: Structure elucidation using transformer models. We convert each spectra into a structured text representation to make it ingestible by the model. Top Right: Generation of spectra from molecules using a transformer model. We reuse the same structured text representation. Bottom right: Predicting functional groups from the spectra as a multilabel multiclass classification problem. We assess transformers, a 1D-CNN and gradient boosted trees.
• Figure 5: Aromatic molecules $^1$H-NMR spectra averaged and plotted against the average nonaromatic molecules $^1$H-NMR spectra