Leffingwell Odor Dataset

NOTE: It's easier to download this dataset from pyrfume. Here's how:

# First install pyrfume in your Python environment. This can be done easily with pip.
# pip install pyrfume

import pyrfume
molecules = pyrfume.load_data('leffingwell/molecules.csv', remote=True)
behavior = pyrfume.load_data('leffingwell/behavior.csv', remote=True)
# e.g. to count the number of molecules with each descriptor
behavior.sum().sort_values(ascending=False).astype(int)  

Predicting properties of molecules is an area of growing research in machine learning, particularly as models for learning from graph-valued inputs improve in sophistication and robustness. A molecular property prediction problem that has received comparatively little attention during this surge in research activity is building Structure-Odor Relationships (SOR) models (as opposed to Quantitative Structure-Activity Relationships, a term from medicinal chemistry). This is a 70+ year-old problem straddling chemistry, physics, neuroscience, and machine learning.

To spur development on the SOR problem, we curated and cleaned a dataset of 3523 molecules associated with expert-labeled odor descriptors from the Leffingwell PMP 2001 database.  We provide featurizations of all molecules in the dataset using bit-based and count-based fingerprints, Mordred molecular descriptors, and the embeddings from our trained GNN model (Sanchez-Lengeling et al., 2019). This dataset is comprised of two files: 

1. leffingwell_data.csv: this contains molecular structures, and what they smell like, along with train, test, and cross-validation splits. More detail on the file structure is found in leffingwell_readme.pdf.
2. leffingwell_embeddings.npz: this contains several featurizations of the molecules in the dataset.
3. leffingwell_readme.pdf: a more detailed description of the data and its provenance, including expected performance metrics.
4. LICENSE: a copy of the CC-BY-NC license language.

The dataset, and all associated features, is freely available for research use under the CC-BY-NC license.

If you use the data in a publication, please cite:

@article{sanchez2019machine,
  title={Machine learning for scent: Learning generalizable perceptual representations of small molecules},
  author={Sanchez-Lengeling, Benjamin and Wei, Jennifer N and Lee, Brian K and Gerkin, Richard C and Aspuru-Guzik, Al{\'a}n and Wiltschko, Alexander B},
  journal={arXiv preprint arXiv:1910.10685},
  year={2019}
}