Initial upload (#1)

- Update README (780289b2a232f548e0dd4b945ce440cea992ae6e)
- Initial upload (75bec4cd903213911ae61c7e432bb1405d852b47)

README.md

@@ -1,3 +1,163 @@
 ---
 license: mit
+task_categories:
+- text-classification
+- text2text-generation
+- translation
+- zero-shot-classification
+tags:
+- chemistry
+- biology
+- SMILES
+- benchmark
+size_categories:
+- 1k<n<10k
+pretty_name: 'Biogen ADME (public data)'
+configs:
+- config_name: full
+  data_files: "biogen-adme.csv.gz"
+- config_name: scaffold-split
+  data_files:
+  - split: train
+    path: "biogen-adme_train.csv.gz"
+  - split: test
+    path: "biogen-adme_test.csv.gz"
 ---
+# Biogen ADME dataset (public data)
+
+Data from [Fang et al., Prospective Validation of Machine Learning Algorithms for Absorption, Distribution, Metabolism, and Excretion Prediction: An Industrial Perspective](https://doi.org/10.1021/acs.jcim.3c00160), available from the [GitHub repositiory](https://github.com/molecularinformatics/Computational-ADME). We used [schemist](https://github.com/scbirlab/schemist) (which in turn uses RDKit)
+to add molecuar weight, Murcko scaffold, Crippen cLogP, and topological surface area, and to generate scaffold splits.
+
+## Dataset Details
+
+From the [original README](https://github.com/molecularinformatics/Computational-ADME):
+
+>
+> To benefit the broader computational chemistry community and improve the > quality and diversity of public-domain ADME data sets we have disclosed a > collection of 3521 diverse compounds selected from commercially available > compound libraries (i.e. Enamine, eMolecules, WuXi LabNetwork, Mcule) and > tested them against our internal six ADME in vitro assays described in this > study using the same experimental conditions as of our in-house datasets.
+> 
+
+### Dataset Description
+
+- **Curated by:** Biogen
+<!-- - **Funded by:** The Francis Crick Institute -->
+- **License:** MIT
+
+### Dataset Sources
+
+<!-- Provide the basic links for the dataset. -->
+
+- **Repository:** https://github.com/molecularinformatics/Computational-ADME
+- **Paper:** https://doi.org/10.1021/acs.jcim.3c00160
+<!-- - **Demo [optional]:** [More Information Needed] -->
+
+## Uses
+
+Benchmarking chemical property prediction models.
+
+<!-- ### Direct Use   -->
+
+<!-- This section describes suitable use cases for the dataset. -->
+
+<!-- [More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
+
+<!-- [More Information Needed]  -->
+
+## Dataset Structure
+
+The train-test splits are generated by scaffold.
+
+The column headings of the data are:
+
+- **SMILES**: Original SMILES string, as in the original data release in the [GitHub repositiory](https://github.com/molecularinformatics/Computational-ADME) 
+- **smiles**: Canonicalized SMILES string
+- **id**: Numeric structure identifier
+- **inchikey**: Unique structure identifier
+- **scaffold**: Murcko scaffold
+- **mwt**: Molecular weight
+- **clogp**: Crippen LogP
+- **tpsa**: Calculated topological polar surface area.
+
+The following columns are ADME properties:
+
+- log_hlm: human liver microsomal (HLM) stability (Clint, mL/min/kg)
+- log_mdr1_mdck_er: MDR1-MDCK efflux ratio
+- log_solubility: solubility at pH 6.8 (ug/mL)
+- log_plasma_protein_binding_human: human plasma protein binding (hPPB) percent unbound 
+- log_plasma_protein_binding_rat: rat plasma protein binding (rPPB) percent unbound
+- log_rlm: rat liver microsomal (RLM) stability (Clint, mL/min/kg)
+
+## Dataset Creation
+
+### Curation Rationale
+
+To make the Biogen ADME dataset readily available with light preprocessing.
+
+#### Data Collection and Processing
+
+Additional properties and scaffold splits were calculated using [schemist](https://github.com/scbirlab/schemist), a tool for processing chemical datasets.
+
+#### Who are the source data producers?
+
+Biogen
+
+#### Personal and Sensitive Information
+
+None
+
+<!-- ## Bias, Risks, and Limitations  -->
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+<!--  [More Information Needed]  -->
+
+<!-- ### Recommendations  -->
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+<!-- Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.  -->
+
+## Citation
+
+<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+```
+@article{doi:10.1021/acs.jcim.3c00160,
+    author = {Fang, Cheng and Wang, Ye and Grater, Richard and Kapadnis, Sudarshan and Black, Cheryl and Trapa, Patrick and Sciabola, Simone},
+    title = {Prospective Validation of Machine Learning Algorithms for Absorption, Distribution, Metabolism, and Excretion  Prediction: An Industrial Perspective},
+    journal = {Journal of Chemical Information and Modeling},
+    volume = {63},
+    number = {11},
+    pages = {3263-3274},
+    year = {2023},
+    doi = {10.1021/acs.jcim.3c00160},
+    note = {PMID: 37216672},
+    URL = {https://doi.org/10.1021/acs.jcim.3c00160},
+    eprint = {https://doi.org/10.1021/acs.jcim.3c00160}
+}
+```
+
+<!-- **APA:** -->
+
+<!-- ## Glossary [optional]  -->
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
+
+<!-- [More Information Needed]
+
+<!-- ## More Information [optional]
+
+<!-- [More Information Needed]
+
+<!-- ## Dataset Card Authors [optional]
+
+<!-- [More Information Needed]  -->
+
+## Dataset Card Contact
+
+[@eachanjohnson](https://huggingface.co/eachanjohnson)

biogen-adme.csv.gz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c103a34f381f18d9a4e0575bb7d7d43c622eb3b143562a29081e3aeceaf0400c
+size 323011

biogen-adme_test.csv.gz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:59be0e95a494a57732c275ac51e91adca461feb10f278314d8b8c2b75909f757
+size 53751

biogen-adme_train.csv.gz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:30f9139d8b6d64743639da2bc203601fe33641a6fe8249325ed1a3e7a0ec519d
+size 270047

convert.log

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+🚀 Converting between string representations with the following parameters:
+	subcommand: convert
+	output: <_io.TextIOWrapper name='<stdout>' mode='w' encoding='utf-8'>
+	format: csv
+	input: <_io.TextIOWrapper name='/nemo/lab/johnsone/home/users/johnsoe/data/datasets/biogen-adme/ADME_public_set_3521-eoj.csv' mode='r' encoding='UTF-8'>
+	representation: SMILES
+	column: SMILES
+	prefix: None
+	to: ['id', 'inchikey', 'smiles', 'scaffold', 'mwt', 'clogp', 'tpsa']
+	options: ['prefix=SCB-']
+	func: <function _convert at 0x7f5b3b793240>
+
+Error counts:
+	id: 0
+	inchikey: 0
+	smiles: 0
+	scaffold: 0
+	mwt: 0
+	clogp: 0
+	tpsa: 0
+⏰ Completed process in 0:00:05.623413

processing.log

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++ schemist convert /nemo/lab/johnsone/home/users/johnsoe/data/datasets/biogen-adme/ADME_public_set_3521-eoj.csv --format csv --column SMILES --to id inchikey smiles scaffold mwt clogp tpsa --options prefix=SCB-
++ schemist split --format csv --column SMILES --type scaffold --train 0.85 --test 0.15 --seed 1
++ gzip --best
++ for split_name in train test
+++ basename /nemo/lab/johnsone/home/users/johnsoe/data/datasets/fang-2023-biogen-adme/biogen-adme.csv.gz .csv.gz
++ split=/nemo/lab/johnsone/home/users/johnsoe/data/datasets/fang-2023-biogen-adme/biogen-adme_train.csv.gz
+++ zcat /nemo/lab/johnsone/home/users/johnsoe/data/datasets/fang-2023-biogen-adme/biogen-adme.csv.gz
+++ head -n1
+++ tr , '
+'
+++ grep -n is_train
+++ cut -d: -f1
++ filter_field=18
++ gzip --best
++ cat /dev/fd/63 /dev/fd/62
+++ zcat /nemo/lab/johnsone/home/users/johnsoe/data/datasets/fang-2023-biogen-adme/biogen-adme.csv.gz
+++ head -n1
+++ zcat /nemo/lab/johnsone/home/users/johnsoe/data/datasets/fang-2023-biogen-adme/biogen-adme.csv.gz
+++ tail -n+2
+++ awk -F, -v OFS=, '$18 == "True"'
++ for split_name in train test
+++ basename /nemo/lab/johnsone/home/users/johnsoe/data/datasets/fang-2023-biogen-adme/biogen-adme.csv.gz .csv.gz
++ split=/nemo/lab/johnsone/home/users/johnsoe/data/datasets/fang-2023-biogen-adme/biogen-adme_test.csv.gz
+++ zcat /nemo/lab/johnsone/home/users/johnsoe/data/datasets/fang-2023-biogen-adme/biogen-adme.csv.gz
+++ head -n1
+++ tr , '
+'
+++ grep -n is_test
+++ cut -d: -f1
++ filter_field=19
++ gzip --best
++ cat /dev/fd/63 /dev/fd/62
+++ zcat /nemo/lab/johnsone/home/users/johnsoe/data/datasets/fang-2023-biogen-adme/biogen-adme.csv.gz
+++ head -n1
+++ zcat /nemo/lab/johnsone/home/users/johnsoe/data/datasets/fang-2023-biogen-adme/biogen-adme.csv.gz
+++ tail -n+2
+++ awk -F, -v OFS=, '$19 == "True"'
++ set +x

split.log

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+🚀 Splitting table with the following parameters:
+	subcommand: split
+	output: <_io.TextIOWrapper name='<stdout>' mode='w' encoding='utf-8'>
+	format: csv
+	input: <_io.TextIOWrapper name='<stdin>' mode='r' encoding='utf-8'>
+	representation: SMILES
+	column: SMILES
+	prefix: None
+	type: scaffold
+	train: 0.85
+	test: 0.15
+	seed: 1
+	func: <function _split at 0x7f55b081f4c0>
+
+
+Split counts:
+	train: 2993
+	test: 528
+	validation: 0
+⏰ Completed process in 0:00:10.966729