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metadata
license: cc-by-4.0
tags:
  - chemistry
  - biology
  - molecular-complexity
  - mass-spectrometry
  - astrobiology
  - drug-discovery
pretty_name: SaganMC
size_categories:
  - 100K<n<1M
dataset_info:
  config_name: sagan-mc-400k
  features:
    - name: inchi
      dtype: string
    - name: inchikey
      dtype: string
    - name: selfies
      dtype: string
    - name: smiles
      dtype: string
    - name: smiles_scaffold
      dtype: string
    - name: formula
      dtype: string
    - name: fingerprint_morgan
      dtype: string
    - name: num_atoms
      dtype: int32
    - name: num_atoms_all
      dtype: int32
    - name: num_bonds
      dtype: int32
    - name: num_bonds_all
      dtype: int32
    - name: num_rings
      dtype: int32
    - name: num_aromatic_rings
      dtype: int32
    - name: physchem_mol_weight
      dtype: float32
    - name: physchem_logp
      dtype: float32
    - name: physchem_tpsa
      dtype: float32
    - name: physchem_qed
      dtype: float32
    - name: physchem_h_acceptors
      dtype: int32
    - name: physchem_h_donors
      dtype: int32
    - name: physchem_rotatable_bonds
      dtype: int32
    - name: physchem_fraction_csp3
      dtype: float32
    - name: mass_spectrum_nist
      dtype: string
    - name: complex_ma_score
      dtype: int32
    - name: complex_ma_runtime
      dtype: float32
    - name: complex_bertz_score
      dtype: float32
    - name: complex_bertz_runtime
      dtype: float32
    - name: complex_boettcher_score
      dtype: float32
    - name: complex_boettcher_runtime
      dtype: float32
    - name: synth_sa_score
      dtype: float32
    - name: meta_cas_number
      dtype: string
    - name: meta_names
      dtype: string
    - name: meta_iupac_name
      dtype: string
    - name: meta_comment
      dtype: string
    - name: meta_origin
      dtype: string
    - name: meta_reference
      dtype: string
    - name: split
      dtype:
        class_label:
          names:
            '0': train
            '1': val
            '2': test
  splits:
    - name: train
      num_bytes: 262218794
      num_examples: 325392
    - name: validation
      num_bytes: 32619164
      num_examples: 40521
    - name: test
      num_bytes: 32805389
      num_examples: 40533
  download_size: 121350317
  dataset_size: 327643347
configs:
  - config_name: sagan-mc-400k
    data_files:
      - split: train
        path: sagan-mc-400k/train-*
      - split: validation
        path: sagan-mc-400k/validation-*
      - split: test
        path: sagan-mc-400k/test-*
    default: true

SaganMC: A Molecular Complexity Dataset with Mass Spectra

Summary

SaganMC is a machine learning-ready dataset designed for molecular complexity prediction, spectral analysis, and chemical discovery. Molecular complexity metrics quantify how structurally intricate a molecule is, reflecting how difficult it is to construct or synthesize.

The dataset includes 406,446 molecules. A subset of 16,653 molecules includes experimental mass spectra. We provide standard representations (SMILES, InChI, SELFIES), RDKit-derived molecular descriptors, Morgan fingerprints, and three complementary complexity scores: Bertz, Böttcher, and the Molecular Assembly Index (MA). MA scores, computed using code from the Cronin Group, are especially relevant to astrobiology research as potential agnostic biosignatures. Assigning MA indices to molecules is compute intensive, and generating this dataset required over 100,000 CPU hours on Google Cloud.

SaganMC is named in honor of Carl Sagan, the astronomer and science communicator whose work inspired generations to explore life beyond Earth. The initial version of this dataset was produced during a NASA Frontier Development Lab (FDL) astrobiology sprint.

Intended Uses

  • Train machine learning models to predict molecular complexity directly from molecular structure or mass spectrometry data.
  • Develop surrogate models to approximate Molecular Assembly Index (MA) scores efficiently at large scale.
  • Benchmark complexity metrics (Bertz, Böttcher, MA) across diverse molecular classes.
  • Enable onboard ML pipelines for spacecraft to prioritize high-complexity chemical targets during exploration.
  • Explore correlations between molecular complexity and experimental observables such as mass spectra.
  • Support AI-driven chemical discovery tasks.

Available Files

  • SaganMC-400k (sagan-mc-400k.csv): The full dataset with 406,446 molecules, including structural and complexity features.
  • SaganMC-Spectra-16k (sagan-mc-spectra-16k.csv): A 16,653-molecule subset of the full dataset, with experimental mass spectra from NIST.

Dataset Structure

Each CSV file includes 36 columns describing various molecular attributes. A split column assigns rows into train, val, or test splits (80/10/10). All data is in CSV format, with string, float, or integer fields.

Features

  • inchi: International Chemical Identifier (InChi).
  • inchikey: Hashed version of the InChI string, used for indexing.
  • selfies: SELFIES (SELF-referencIng Embedded Strings) representation of the molecule.
  • smiles: SMILES (Simplified Molecular Input Line Entry System) representation of the molecule.
  • smiles_scaffold: Murcko scaffold representation extracted from the molecule.
  • formula: Molecular formula.
  • fingerprint_morgan: Base64-encoded 2048-bit Morgan fingerprint (ECFP4) with chirality.
  • num_atoms: Number of heavy atoms (excluding hydrogens).
  • num_atoms_all: Total number of atoms (including hydrogens).
  • num_bonds: Number of bonds between heavy atoms.
  • num_bonds_all: Total number of bonds (including to hydrogens).
  • num_rings: Number of rings in the molecule.
  • num_aromatic_rings: Number of aromatic rings in the molecule.
  • physchem_mol_weight: Molecular weight (Daltons).
  • physchem_logp: LogP, a measure of lipophilicity.
  • physchem_tpsa: Topological Polar Surface Area, related to hydrogen bonding.
  • physchem_qed: Quantitative Estimate of Drug-likeness.
  • physchem_h_acceptors: Number of hydrogen bond acceptors.
  • physchem_h_donors: Number of hydrogen bond donors.
  • physchem_rotatable_bonds: Number of rotatable bonds.
  • physchem_fraction_csp3: Fraction of sp3-hybridized carbon atoms.
  • mass_spectrum_nist: Mass spectrum data sourced from the NIST Chemistry WebBook, encoded in JCAMP-DX format as a string. Includes metadata, units, and a peak table.
  • complex_ma_score: Molecular Assembly Index score (pathway complexity).
  • complex_ma_runtime: Wall-clock runtime (in seconds) to compute MA score.
  • complex_bertz_score: Bertz/Hendrickson/Ihlenfeldt (BHI) complexity score.
  • complex_bertz_runtime: Wall-clock runtime (in seconds) to compute BHI score.
  • complex_boettcher_score: Böttcher complexity score, based on atom environments.
  • complex_boettcher_runtime: Wall-clock runtime (in seconds) to compute Böttcher score.
  • synth_sa_score: Synthetic accessibility score (SAScore)
  • meta_cas_number: CAS Registry Number, if available.
  • meta_names: Common names or synonyms for the molecule.
  • meta_iupac_name: IUPAC name for the molecule.
  • meta_comment: Optional comments associated with the molecule.
  • meta_origin: Source or origin information for the molecule.
  • meta_reference: Reference or source citation for the molecule.
  • split: Predefined data split (train, val, test).

Data Sources and Tools Used

Citation

Please cite the following if you use this dataset:

@inproceedings{gebhard-2022-molecular,
  title = {Inferring molecular complexity from mass spectrometry data using machine learning},
  author = {Gebhard, Timothy D. and Bell, Aaron and Gong, Jian and Hastings, Jaden J.A. and Fricke, George M. and Cabrol, Nathalie and Sandford, Scott and Phillips, Michael and Warren-Rhodes, Kimberley and Baydin, {Atılım Güneş}},
  booktitle = {Machine Learning and the Physical Sciences workshop, NeurIPS 2022},
  year = {2022}
}

Acknowledgments

This work was enabled by and carried out during an eight-week research sprint as part of the Frontier Development Lab (FDL), a public-private partnership between NASA, the U.S. Department of Energy, the SETI Institute, Trillium Technologies, and leaders in commercial AI, space exploration, and Earth sciences, formed with the purpose of advancing the application of machine learning, data science, and high performance computing to problems of material concern to humankind.

We thank Google Cloud and the University of New Mexico Center for Advanced Research Computing for providing the compute resources critical to completing this work. GMF was funded by NASA Astrobiology NfoLD grant #80NSSC18K1140. We also thank the Cronin Group at the University of Glasgow for their collaboration, and for providing us with the code for computing MA values.

License

Creative Commons Attribution 4.0 International (CC BY 4.0)