Create README.md

README.md

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+---
+language:
+- en
+tags:
+- chemistry
+- biology
+pretty_name: >-
+  Processed NCI Open Compounds Structures for Docking, Cofold, and Affinity
+  Prediction
+size_categories:
+- 100K<n<1M
+configs:
+- config_name: default
+  data_files: "nci_compounds.tsv"
+---
+
+# Curated ProteinMPNN training dataset
+
+A curated set of the NCI Open Compounds with compatible mol2 and pdbqt files safe for cofolding and docking applications
+
+## Quickstart Usage
+
+### Install HuggingFace Datasets package
+
+Each subset can be loaded into python using the Huggingface [datasets](https://huggingface.co/docs/datasets/index) library.
+First, from the command line install the `datasets` library
+
+    $ pip install datasets
+
+Optionally set the cache directory, e.g.
+
+    $ HF_HOME=${HOME}/.cache/huggingface/
+    $ export HF_HOME
+
+then, from within python load the datasets library
+
+    >>> import datasets
+
+### Load model datasets
+   
+To load one of the `NCI_Open_Compounds` model datasets, use `datasets.load_dataset(...)`:
+
+    >>> dataset_tag = "train"
+    >>> dataset_models = datasets.load_dataset(
+      path = "leebecca/group_mpnn",
+      name = f"{dataset_tag}_models",
+      data_dir = f"{dataset_tag}")['train']
+
+and the dataset is loaded as a `datasets.arrow_dataset.Dataset`
+
+    >>> dataset_models
+    Dataset({
+        features: [    
+          'NSC',
+          'duplicate_idx',
+          'CID',
+          'SID',
+          'CAS',
+          'entry_id',
+          'entry_name',
+          'name',
+          'formula',
+          'smiles',
+          'mw',
+          'tot_q',
+          'tot_abs_q',
+          'chiralities_consistent',
+          'chiral_flag',
+          'flags',
+          'charging_adjusted_penalty',
+          'ionization_penalty',
+          'ionization_penalty_charging',
+          'ionization_penalty_neutral',
+          'state_penalty',
+          'energy',
+          'tautomer_probability',
+          'input_file',
+          'structure_evaluation',
+          'chemistry_notes',
+          'pka_notes'
+        ],
+        num_rows: 445794
+    })
+
+
+## Dataset Details
+
+### Dataset Description
+The set contains ligprep output of the minimized 3D structures, expanded to include possible protonation states and tautomers capped at 3 per ligand.
+
+
+- **Acknowledgements:**
+We kindly acknowledge the ProteinMPNN team, RosettaCommons, and the following institutions: University of California, Los Angeles; University of Maryland; University of Oregon; University of Michigan; University of Pennsylvania; and the Wistar Institute.
+
+
+### Dataset Sources
+https://wiki.nci.nih.gov/spaces/NCIDTPdata/pages/155844992/Chemical+Data
+
+## Uses
+
+### Out-of-Scope Use
+
+
+### Source Data
+
+## Citation
+
+## Dataset Card Authors
+Miranda Simpson (miranda13nicoles@gmail.com), Becca Lee (beccalee5@g.ucla.edu), Nathaniel Felbinger (nfelbing@umd.edu), Pratyush Dhal (pdhal@umich.edu), Colby Agostino (colby.agostino@pennmedicine.upenn.edu)