Leffingwell Odor Dataset

<p><strong>NOTE: It's easier to download this dataset from <a href="https://github.com/pyrfume/pyrfume">pyrfume</a>. Here's how:</strong></p>\n\n<pre><code># First install pyrfume in your Python environment. This can be done easily with pip.\n# pip install pyrfume\n\nimport pyrfume\nmolecules = pyrfume.load_data('leffingwell/molecules.csv', remote=True)\nbehavior = pyrfume.load_data('leffingwell/behavior.csv', remote=True)\n# e.g. to count the number of molecules with each descriptor\nbehavior.sum().sort_values(ascending=False).astype(int) \n\n</code></pre>\n\n<p>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.</p>\n\n<p>To spur development on the SOR problem, we curated and cleaned a dataset of 3523 molecules associated with expert-labeled odor descriptors from the <em>Leffingwell PMP 2001</em> 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: </p>\n\n<ol>\n\t<li><strong>leffingwell_data.csv</strong>: 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.</li>\n\t<li><strong>leffingwell_embeddings.npz</strong>: this contains several featurizations of the molecules in the dataset.</li>\n\t<li><strong>leffingwell_readme.pdf</strong>: a more detailed description of the data and its provenance, including expected performance metrics.</li>\n\t<li><strong>LICENSE</strong>: a copy of the CC-BY-NC license language.</li>\n</ol>\n\n<p>The dataset, and all associated features, is freely available for research use under the <a href="https://creativecommons.org/licenses/by-nc/4.0/">CC-BY-NC</a> license.</p>\n\n<p>If you use the data in a publication, please cite:</p>\n\n<pre>@article{sanchez2019machine,\n title={Machine learning for scent: Learning generalizable perceptual representations of small molecules},\n 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},\n journal={arXiv preprint arXiv:1910.10685},\n year={2019}\n}</pre>

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