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README.md

@@ -1,167 +1,153 @@
----
-license: mit
-dataset_info:
-- config_name: ADME
-  features:
-  - name: Task
-    dtype: string
-  - name: Drug_ID
-    dtype: string
-  - name: SMILES
-    dtype: string
-  - name: Y
-    dtype: float64
-  - name: split
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 11857638
-    num_examples: 111612
-  download_size: 4050495
-  dataset_size: 11857638
-- config_name: CRISPROutcome
-  features:
-  - name: Task
-    dtype: string
-  - name: GuideSeq_ID
-    dtype: string
-  - name: GuideSeq
-    dtype: string
-  - name: Y
-    dtype: float64
-  - name: split
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 681958
-    num_examples: 7605
-  download_size: 215266
-  dataset_size: 681958
-- config_name: Develop
-  features:
-  - name: Task
-    dtype: string
-  - name: Antibody_ID
-    dtype: string
-  - name: heavy_chain
-    dtype: string
-  - name: light_chain
-    dtype: string
-  - name: Y
-    dtype: float64
-  - name: split
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 732537
-    num_examples: 3614
-  download_size: 235181
-  dataset_size: 732537
-- config_name: Epitope
-  features:
-  - name: Task
-    dtype: string
-  - name: Antigen_ID
-    dtype: string
-  - name: Antigen
-    dtype: string
-  - name: Y
-    dtype: string
-  - name: split
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 2504514
-    num_examples: 3606
-  download_size: 2179200
-  dataset_size: 2504514
-- config_name: HTS
-  features:
-  - name: Task
-    dtype: string
-  - name: Drug_ID
-    dtype: string
-  - name: SMILES
-    dtype: string
-  - name: Y
-    dtype: int64
-  - name: split
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 234479816
-    num_examples: 2053052
-  download_size: 58513481
-  dataset_size: 234479816
-- config_name: QM
-  features:
-  - name: Task
-    dtype: string
-  - name: Drug_ID
-    dtype: string
-  - name: Atom
-    dtype: string
-  - name: x_coordinate
-    dtype: float64
-  - name: y_coordinate
-    dtype: float64
-  - name: z_coordinate
-    dtype: float64
-  - name: Y
-    dtype: float64
-  - name: split
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 2896357941
-    num_examples: 41522485
-  download_size: 1249890104
-  dataset_size: 2896357941
-- config_name: Tox
-  features:
-  - name: Task
-    dtype: string
-  - name: Drug_ID
-    dtype: string
-  - name: SMILES
-    dtype: string
-  - name: Y
-    dtype: float64
-  - name: split
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 258380329
-    num_examples: 2563685
-  download_size: 78889561
-  dataset_size: 258380329
-configs:
-- config_name: ADME
-  data_files:
-  - split: train
-    path: single_instance_datasets/ADME/train-*
-- config_name: CRISPROutcome
-  data_files:
-  - split: train
-    path: single_instance_datasets/CRISPROutcome/train-*
-- config_name: Develop
-  data_files:
-  - split: train
-    path: single_instance_datasets/Develop/train-*
-- config_name: Epitope
-  data_files:
-  - split: train
-    path: single_instance_datasets/Epitope/train-*
-- config_name: HTS
-  data_files:
-  - split: train
-    path: single_instance_datasets/HTS/train-*
-- config_name: QM
-  data_files:
-  - split: train
-    path: single_instance_datasets/QM/train-*
-- config_name: Tox
-  data_files:
-  - split: train
-    path: single_instance_datasets/Tox/train-*
----
+---
+verison: 1.0.0
+license: mit
+task_categories:
+- tabular-classification
+- tabular-regression
+language:
+- en
+tags:
+- bioactivity
+- therapeutic science
+pretty_name: Therapeutics Data Commons
+size_categories:
+- 10M<n<100M
+dataset_summary: >-
+  Therapeutics Data Commons (TDC) provides curated, AI-ready datasets, machine
+  learning tasks, and benchmarks with meaningful data splits, supporting the
+  development and evaluation of AI methods for therapeutic discovery. TDC tasks
+  are categorized into three main problem types: (1) single-instance prediction,
+  (2) multi-instance learning, and (3) molecule generation.
+citation: >-
+  @article{Huang2021tdc,
+    title={Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development},
+    author={Huang, Kexin and Fu, Tianfan and Gao, Wenhao and Zhao, Yue and Roohani, Yusuf and Leskovec, Jure and Coley, Connor W and Xiao, Cao and Sun, Jimeng and Zitnik, Marinka},
+    journal={Proceedings of Neural Information Processing Systems, NeurIPS Datasets and Benchmarks},
+    year={2021}
+  }
+
+  @article{Huang2022artificial,
+    title={Artificial intelligence foundation for therapeutic science},
+    author={Huang, Kexin and Fu, Tianfan and Gao, Wenhao and Zhao, Yue and Roohani, Yusuf and Leskovec, Jure and Coley, Connor W and Xiao, Cao and Sun, Jimeng and Zitnik, Marinka},
+    journal={Nature Chemical Biology},
+    year={2022}
+  }
+
+  @article{velez-arce2024signals,
+    title={Signals in the Cells: Multimodal and Contextualized Machine Learning Foundations for Therapeutics},
+    author={Velez-Arce, Alejandro and Lin, Xiang and Huang, Kexin and Li, Michelle M and Gao, Wenhao and Pentelute, Bradley and Fu, Tianfan and Kellis, Manolis and Zitnik, Marinka},
+    booktitle={NeurIPS 2024 Workshop on AI for New Drug Modalities},
+    year={2024}
+  }
+
+# Single instance prediciton  
+config_names:
+- ADME
+- Tox
+- HTS
+- CRISPROutcome
+- Develop
+- Epitope
+- QM
+configs:
+- config_name: ADME
+  data_files: single_instance_prediction_datasets/ADME.parquet
+  columns:
+  - Task
+  - Drug_ID
+  - SMILES
+  - 'Y'
+  - split
+- config_name: Tox
+  data_files: single_instance_prediction_datasets/Tox.parquet
+  columns:
+  - Task
+  - Drug_ID
+  - SMILES
+  - 'Y'
+  - split
+- config_name: HTS
+  data_files: single_instance_prediction_datasets/HTS.parquet
+  columns:
+  - Task
+  - Drug_ID
+  - SMILES
+  - 'Y'
+  - split
+- config_name: CRISPROutcome
+  data_files: single_instance_prediction_datasets/CRISPROutcome.parquet
+  columns:
+  - Task
+  - GuideSeq_ID
+  - GuideSeq
+  - 'Y'
+  - split
+- config_name: Develop
+  data_files: single_instance_prediction_datasets/Develop.parquet
+  columns:
+  - Task
+  - Antibody_ID
+  - heavy_chain
+  - light_chain
+  - 'Y'
+  - split
+- config_name: Epitope
+  data_files: single_instance_prediction_datasets/Epitope.parquet
+  columns:
+  - Task
+  - Antigen_ID
+  - Antigen
+  - 'Y'
+  - split
+- config_name: QM
+  data_files: single_instance_prediction_datasets/QM.parquet
+  columns:
+  - Task
+  - Drug_ID
+  - Atom
+  - x_coordinate
+  - y_coordinate
+  - z_coordinate
+  - 'Y'
+  - split
+
+
+---
+# Therapeutics Data Commons
+[Therapeutics Data Commons](https://tdcommons.ai/)(TDC)  provides a publicly available collection of 22 machine learning tasks for therapeutic discovery. Our Hugging Face repository is a mirror of single-instance prediction tasks of TDC, encompassing a total of 46,265,659 data points.
+
+The parquet files uploaded to our Hugging Face repository have been sanitized and curated from the original datasets.
+- Each parquet file corresponds to a separate category (e.g., ADME) and contains multiple tasks (e.g., solubility, permeability).
+- Each file follows its own schema (i.e., different column names) and has been preprocessed differently depending on the category.
+- As ADME, Tox, and HTS datasets contain SMILES strings, we have sanitized (standardized) the SMILES strings. The sanitization process includes removing salts, standardizing the SMILES strings to a canonical form, etc. 2 invalid SMILES strings from ADME dataset and 59 invalid SMILES strings from HTS dataset were removed during the sanitization process. 
+
+A summary file (i.e., single_instance_prediction_summary.csv) is also uploaded, which lists:
+  -  Problem (Category),
+  -  Task,
+  -  DatasetName,
+  -  NumCompounds,
+  -  Leaderboard information (Unit, Task, Metric, Dataset Split - https://tdcommons.ai/benchmark/admet_group/overview/)
+  -  License,
+  -  DatasetDescription,
+  -  TaskDescription,
+  -  Reference
+
+
+
+## Quick Usage
+Load a dataset in python
+
+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
+
+then, from within python load the datasets library.
+
+    >>> import datasets
+
+Now load one of the 'TDC' datasets, e.g.,
+
+    >>> dataset = datasets.load_dataset("maomlab/TDC", name = "ADME")
+
+You can modify "name" based on your interest (e.g., "Tox").