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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 3 new columns ({'SMILES', 'IDType', 'ID'}) and 7 missing columns ({'NumActiveCompounds', 'Description', 'URL', 'Reference', 'Paper Title', 'Assay Name', 'AID_confirmatory'}).
This happened while the csv dataset builder was generating data using
hf://datasets/maomlab/ChAFF/data/Absorbance.csv (at revision da0d7cd86fa09912e506f0889c911408061b0697)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1831, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 644, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2272, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
Type: string
DatasetName: double
AID: int64
ID: int64
IDType: string
SMILES: string
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 921
to
{'Type': Value('string'), 'DatasetName': Value('string'), 'AID': Value('float64'), 'AID_confirmatory': Value('float64'), 'NumActiveCompounds': Value('int64'), 'Paper Title': Value('string'), 'Reference': Value('string'), 'URL': Value('string'), 'Assay Name': Value('string'), 'Description': Value('string')}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1456, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1055, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 894, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 970, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1702, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1833, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 3 new columns ({'SMILES', 'IDType', 'ID'}) and 7 missing columns ({'NumActiveCompounds', 'Description', 'URL', 'Reference', 'Paper Title', 'Assay Name', 'AID_confirmatory'}).
This happened while the csv dataset builder was generating data using
hf://datasets/maomlab/ChAFF/data/Absorbance.csv (at revision da0d7cd86fa09912e506f0889c911408061b0697)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Type
string | DatasetName
null | AID
float64 | AID_confirmatory
null | NumActiveCompounds
int64 | Paper Title
string | Reference
string | URL
null | Assay Name
string | Description
string |
|---|---|---|---|---|---|---|---|---|---|
Absorbance
| null | 632 | null | 43 |
A Novel Class of Small Molecule Inhibitors of Hsp90
|
(Yi, 2008, 10.1021/cb800162x)
| null |
Confirmation Concentration-Response Assay and Counterscreen for Disrupters of an Hsp90 Co-Chaperone Interaction
|
Interference compounds have a score of 10
|
Absorbance
| null | 1,641 | null | 92 |
Identification of Compounds That Interfere with High-Throughput Screening Assay Technologies
|
(David, 2019, 10.1002/cmdc.201900395)
| null |
TR-FRET counterscreen for FAK inhibitors: dose-response biochemical high throughput screening assay to identify inhibitors of Proline-rich tyrosine kinase 2 (Pyk2)
|
TR-FRET counterscreen for FAK inhibitors
|
Absorbance
| null | 1,730 | null | 10 |
Identification of Compounds That Interfere with High-Throughput Screening Assay Technologies
|
(David, 2019, 10.1002/cmdc.201900395)
| null |
Counterscreen Assay for Inhibitors of the ERK Signaling Pathway using a Homogeneous Screening Assay
|
Counterscreen Assay for Inhibitors of the ERK Signaling Pathway using a Homogeneous Screening Assay
|
Absorbance
| null | 1,857 | null | 290 | null | null | null |
FRET-based counterscreen assay for selective VIM-2 inhibitors: biochemical high throughput screening assay to identify epi-absorbance assay artifacts
|
FRET-based counterscreen assay to identify epi-absorbance assay artifacts
|
Absorbance
| null | 1,926 | null | 118 | null | null | null |
FRET-based counterscreen for selective VIM-2 inhibitors: dose response biochemical high throughput screening assay to identify epi-absorbance assay artifacts.
|
FRET-based counterscreen assay to identify epi-absorbance assay artifacts
|
Absorbance
| null | 435,026 | null | 507 |
Identification of Compounds That Interfere with High-Throughput Screening Assay Technologies
|
(David, 2019, 10.1002/cmdc.201900395)
| null |
Fluorescence Cell-Free Homogeneous Counterscreen to Identify Inhibitors of the RanGTP-Importin-beta complex
|
fluorescence resonance energy transfer (FRET)-based biochemical assay used as counterscreen
|
Absorbance
| null | 504,689 | null | 59 |
Identification of Compounds That Interfere with High-Throughput Screening Assay Technologies
|
(David, 2019, 10.1002/cmdc.201900395)
| null |
Dose Response confirmation of UBC13 Polyubiquitin Inhibitors using a Bfl-1 counterscreen
|
uHTS identification of UBC13 Polyubiquitin Inhibitors via a TR-FRET Assay
|
Absorbance
| null | 720,541 | null | 416 |
Identification of Compounds That Interfere with High-Throughput Screening Assay Technologies
|
(David, 2019, 10.1002/cmdc.201900395)
| null |
qHTS for Inhibitors of the Phosphatase Activity of Eya2: Carboxyl-terminal binding protein (CtBP) Counterscreen for Cherry-picked Compounds
|
Carboxyl-terminal binding protein (CtBP) Counterscreen for Cherry-picked Compounds
|
Absorbance
| null | 1,159,604 | null | 51 |
Identification of Compounds That Interfere with High-Throughput Screening Assay Technologies
|
(David, 2019, 10.1002/cmdc.201900395)
| null |
Counterscreen for inhibitors of ASK1: AlphaScreen-based biochemical high throughput dose response assay to identify inhibitors that optically interfere with alphascreen assays using TruHits beads
|
Counterscreen for inhibitors of ASK1: AlphaScreen-based biochemical high throughput dose response assay to identify inhibitors that optically interfere with alphascreen assays using TruHits beads
|
Artifact
| null | 485,270 | null | 701 |
Machine Learning Assisted Hit Prioritization for High Throughput Screening in Drug Discovery
|
(Boldini, 2024, 10.1021/acscentsci.3c01517)
| null | null |
One of Boldini2024 dataset which contains 13 different subdatasets.
|
Artifact
| null | 1,040 | null | 501 |
Machine Learning Assisted Hit Prioritization for High Throughput Screening in Drug Discovery
|
(Boldini, 2024, 10.1021/acscentsci.3c01517)
| null | null |
One of Boldini2024 dataset which contains 13 different subdatasets.
|
Artifact
| null | 628 | null | 1,396 |
Machine Learning Assisted Hit Prioritization for High Throughput Screening in Drug Discovery
|
(Boldini, 2024, 10.1021/acscentsci.3c01517)
| null | null |
One of Boldini2024 dataset which contains 13 different subdatasets.
|
Artifact
| null | 1,672 | null | 942 |
Machine Learning Assisted Hit Prioritization for High Throughput Screening in Drug Discovery
|
(Boldini, 2024, 10.1021/acscentsci.3c01517)
| null | null |
One of Boldini2024 dataset which contains 13 different subdatasets.
|
Artifact
| null | 449,739 | null | 940 |
Machine Learning Assisted Hit Prioritization for High Throughput Screening in Drug Discovery
|
(Boldini, 2024, 10.1021/acscentsci.3c01517)
| null | null |
One of Boldini2024 dataset which contains 13 different subdatasets.
|
Artifact
| null | 2,239 | null | 175 |
Machine Learning Assisted Hit Prioritization for High Throughput Screening in Drug Discovery
|
(Boldini, 2024, 10.1021/acscentsci.3c01517)
| null | null |
One of Boldini2024 dataset which contains 13 different subdatasets.
|
Artifact
| null | 2,661 | null | 191 |
Machine Learning Assisted Hit Prioritization for High Throughput Screening in Drug Discovery
|
(Boldini, 2024, 10.1021/acscentsci.3c01517)
| null | null |
One of Boldini2024 dataset which contains 13 different subdatasets.
|
Artifact
| null | 488,975 | null | 383 |
Machine Learning Assisted Hit Prioritization for High Throughput Screening in Drug Discovery
|
(Boldini, 2024, 10.1021/acscentsci.3c01517)
| null | null |
One of Boldini2024 dataset which contains 13 different subdatasets.
|
Artifact
| null | 504,558 | null | 48 |
Machine Learning Assisted Hit Prioritization for High Throughput Screening in Drug Discovery
|
(Boldini, 2024, 10.1021/acscentsci.3c01517)
| null | null |
One of Boldini2024 dataset which contains 13 different subdatasets.
|
Artifact
| null | 2,098 | null | 923 |
Machine Learning Assisted Hit Prioritization for High Throughput Screening in Drug Discovery
|
(Boldini, 2024, 10.1021/acscentsci.3c01517)
| null | null |
One of Boldini2024 dataset which contains 13 different subdatasets.
|
Artifact
| null | 624,304 | null | 1,150 |
Machine Learning Assisted Hit Prioritization for High Throughput Screening in Drug Discovery
|
(Boldini, 2024, 10.1021/acscentsci.3c01517)
| null | null |
One of Boldini2024 dataset which contains 13 different subdatasets.
|
Artifact
| null | 485,273 | null | 1,119 |
Machine Learning Assisted Hit Prioritization for High Throughput Screening in Drug Discovery
|
(Boldini, 2024, 10.1021/acscentsci.3c01517)
| null | null |
One of Boldini2024 dataset which contains 13 different subdatasets.
|
Artifact
| null | 873 | null | 853 |
Machine Learning Assisted Hit Prioritization for High Throughput Screening in Drug Discovery
|
(Boldini, 2024, 10.1021/acscentsci.3c01517)
| null | null |
One of Boldini2024 dataset which contains 13 different subdatasets.
|
Artifact
| null | 2,221 | null | 243 |
Machine Learning Assisted Hit Prioritization for High Throughput Screening in Drug Discovery
|
(Boldini, 2024, 10.1021/acscentsci.3c01517)
| null | null |
One of Boldini2024 dataset which contains 13 different subdatasets.
|
Artifact
| null | 720,511 | null | 548 |
Machine Learning Assisted Hit Prioritization for High Throughput Screening in Drug Discovery
|
(Boldini, 2024, 10.1021/acscentsci.3c01517)
| null | null |
One of Boldini2024 dataset which contains 13 different subdatasets.
|
Artifact
| null | 686,996 | null | 393 |
Machine Learning Assisted Hit Prioritization for High Throughput Screening in Drug Discovery
|
(Boldini, 2024, 10.1021/acscentsci.3c01517)
| null | null |
One of Boldini2024 dataset which contains 13 different subdatasets.
|
Artifact
| null | 1,832 | null | 446 |
Machine Learning Assisted Hit Prioritization for High Throughput Screening in Drug Discovery
|
(Boldini, 2024, 10.1021/acscentsci.3c01517)
| null | null |
One of Boldini2024 dataset which contains 13 different subdatasets.
|
Autofluoresence
| null | 587 | null | 34 |
Fluorescence spectroscopic profiling of compound libraries
|
(Simeonov, 2008, 10.1021/jm701301m)
| null |
qHTS Assay for Spectroscopic Profiling in Texas Red Spectral Region
| null |
Autofluoresence
| null | 588 | null | 25 |
Fluorescence spectroscopic profiling of compound libraries
|
(Simeonov, 2008, 10.1021/jm701301m)
| null |
qHTS Assay for Spectroscopic Profiling in Resorufin Spectral Region
| null |
Autofluoresence
| null | 589 | null | 6,214 |
Fluorescence spectroscopic profiling of compound libraries
|
(Simeonov, 2008, 10.1021/jm701301m)
| null |
qHTS Assay for Spectroscopic Profiling in 4-MU Spectral Region
| null |
Autofluoresence
| null | 590 | null | 6,427 |
Fluorescence spectroscopic profiling of compound libraries
|
(Simeonov, 2008, 10.1021/jm701301m)
| null |
qHTS Assay for Spectroscopic Profiling in A350 Spectral Region
| null |
Autofluoresence
| null | 591 | null | 59 |
Fluorescence spectroscopic profiling of compound libraries
|
(Simeonov, 2008, 10.1021/jm701301m)
| null |
qHTS Assay for Spectroscopic Profiling in A488 Spectral Region
|
A488 Spectral Region; 480 nm and 540 nm excitation and emission
|
Autofluoresence
| null | 592 | null | 0 |
Fluorescence spectroscopic profiling of compound libraries
|
(Simeonov, 2008, 10.1021/jm701301m)
| null |
qHTS Assay for Spectroscopic Profiling in A647 Spectral Region
| null |
Autofluoresence
| null | 593 | null | 532 |
Fluorescence spectroscopic profiling of compound libraries
|
(Simeonov, 2008, 10.1021/jm701301m)
| null |
qHTS Assay for Spectroscopic Profiling in Fluorescein Spectral Region
|
480 nm and 540 nm excitation and emission
|
Autofluoresence
| null | 594 | null | 32 |
Fluorescence spectroscopic profiling of compound libraries
|
(Simeonov, 2008, 10.1021/jm701301m)
| null |
qHTS Assay for Spectroscopic Profiling in Rhodamine Spectral Region
| null |
Autofluoresence
| null | 709 | null | 3,590 | null | null | null |
Profiling the NIH Molecular Libraries Small Molecule Repository: Autofluorescence at 339/460 nm
|
Autofluorescence at 339/460 nm; For fluorescent compounds, Dye equivalent concentration at given concentration > 10μm/ Fold Increase at 10μm; Blue non-fluorescent compounds need to be validated more than three times, while green non-fluorescent compounds should be validated more than five times.
|
Autofluoresence
| null | 923 | null | 779 |
A small molecule inhibitor of Caspase 1
|
(Boxer, 2010, https://www.ncbi.nlm.nih.gov/books/NBK56241/)
| null |
qHTS Assay for Allosteric/Competitive Inhibitors of Caspase-1: Spectroscopic Profiling in AFC Spectral Region
|
405 nm excitation/520 nm emission
|
Autofluoresence
| null | 1,480 | null | 89 |
Title: Selective Efflux Inhibition of ATP-binding Cassette Sub-family G Member 2
|
(Strouse, 2010, https://pubmed.ncbi.nlm.nih.gov/23658968/)
| null |
Profiling compound fluorescence in IgMXP3 at 488/530 nm; counter screen to single point confirmation of ABCG2 screen
|
in IgMXP3 cells (ABCG2) 488/530 nm
|
Autofluoresence
| null | 1,483 | null | 83 |
Title: Selective Efflux Inhibition of ATP-binding Cassette Sub-family G Member 2
|
(Strouse, 2010, https://pubmed.ncbi.nlm.nih.gov/23658968/)
| null |
Profiling compound fluorescence in CCRF-Adr at 488/530 nm; counter screen to single point confirmation of ABCB1 screen
|
in CCRF-Adr (ABCB1 expressing cells) at 488/530 nm
|
Autofluoresence
| null | 1,696 | null | 342 |
Identification of triazinoindol-benzimidazolones as nanomolar inhibitors of the Mycobacterium tuberculosis enzyme TDP-6-deoxy- D-xylo-4-hexopyranosid-4-ulose 3,5-epimerase (RmlC)
|
(Sivendran, 2010, 10.1016/j.bmc.2009.11.033)
| null |
Rml C and D fluorescent artifact dose-response confirmation
|
340/460 nm
|
Autofluoresence
| null | 1,775 | null | 825 | null | null | null |
Profiling compound fluorescence on Avidin Beads with 488 nm excitation and 530 nm emission
|
on Avidin Beads with 488 nm excitation and 530 nm emission
|
Autofluoresence
| null | 1,776 | null | 489 | null | null | null |
Profiling compound fluorescence on GSH Beads with 488 nm excitation and 530 nm emission
|
on GSH Beads with 488 nm excitation and 530 nm emission
|
Autofluoresence
| null | 2,124 | null | 198 | null | null | null |
Counterscreen for Fluorescence in GFP-Spectra Wavelengths Caused By Cell-Permeable Autofluorescent Compounds
|
488nm excitation / 535 nm emission
|
Autofluoresence
| null | 2,757 | null | 9 | null | null | null |
Test compound autofluorescence in Saccharomyes cerevisiae specifically s288c
|
520 nm emission in Saccharomyes cerevisiae s288c
|
Autofluoresence
| null | 588,517 | null | 63 | null | null | null |
Compound fluorescence counter screen for HTS for inhibitors of efflux pump with Cherry Pick1 compound set
|
488 nm excitation
|
Autofluoresence
| null | 588,620 | null | 13 | null | null | null |
Dose response compound fluorescence counter screen for HTS for inhibitors of efflux pump with Hit compounds from Cherry Pick1
|
488 nm excitation
|
Autofluoresence
| null | 624,483 | null | 10,930 | null | null | null |
Counterscreen of compound fluorescence effects on High-throughput multiplex microsphere screening for inhibitors of toxin protease
|
Spherotech Blue Array Particle kit
|
Autofluoresence
| null | 720,675 | null | 21 | null | null | null |
qHTS assay to test for compound auto fluorescence at 535 nm (green) in HepG2 cells
|
535 nm (green) in HepG2 cells
|
Autofluoresence
| null | 720,678 | null | 35 | null | null | null |
qHTS assay to test for compound auto fluorescence at 460 nm (blue) in HEK293 cells
|
460 nm (blue) in HEK293 cells
|
Autofluoresence
| null | 720,680 | null | 13 | null | null | null |
qHTS assay to test for compound auto fluorescence at 535 nm (green) in HEK293 cells
|
535 nm (green) in HEK293 cells
|
Autofluoresence
| null | 720,681 | null | 37 | null | null | null |
qHTS assay to test for compound auto fluorescence at 460 nm (blue) in HEK293 cell free culture
|
460 nm (blue) in HEK293 cell
|
Autofluoresence
| null | 720,682 | null | 13 | null | null | null |
qHTS assay to test for compound auto fluorescence at 535 nm (green) in HEK293 cell free culture
|
535 nm (green) in HEK293 cell
|
Autofluoresence
| null | 720,686 | null | 20 | null | null | null |
qHTS assay to test for compound auto fluorescence at 535 nm (green) in HepG2 cell free culture
|
535 nm (green) in HepG2 cell
|
Autofluoresence
| null | 720,687 | null | 32 | null | null | null |
qHTS assay to test for compound auto fluorescence at 460 nm (blue) in HepG2 cells
|
460 nm (blue) in HepG2 cells
|
Autofluoresence
| null | null | null | 216 |
High-Throughput Screening to Predict Chemical-Assay Interference
|
(Borrel, 2020, 10.1038/s41598-020-60747-3)
| null | null |
One of tox21-spec-hepg2-p dataset which contains 6 different subdatasets.
|
Autofluoresence
| null | null | null | 80 |
High-Throughput Screening to Predict Chemical-Assay Interference
|
(Borrel, 2020, 10.1038/s41598-020-60747-3)
| null | null |
One of tox21-spec-hepg2-p dataset which contains 6 different subdatasets.
|
Autofluoresence
| null | null | null | 39 |
High-Throughput Screening to Predict Chemical-Assay Interference
|
(Borrel, 2020, 10.1038/s41598-020-60747-3)
| null | null |
One of tox21-spec-hepg2-p dataset which contains 6 different subdatasets.
|
Autofluoresence
| null | null | null | 209 |
High-Throughput Screening to Predict Chemical-Assay Interference
|
(Borrel, 2020, 10.1038/s41598-020-60747-3)
| null | null |
One of tox21-spec-hepg2-p dataset which contains 6 different subdatasets.
|
Autofluoresence
| null | null | null | 80 |
High-Throughput Screening to Predict Chemical-Assay Interference
|
(Borrel, 2020, 10.1038/s41598-020-60747-3)
| null | null |
One of tox21-spec-hepg2-p dataset which contains 6 different subdatasets.
|
Autofluoresence
| null | null | null | 37 |
High-Throughput Screening to Predict Chemical-Assay Interference
|
(Borrel, 2020, 10.1038/s41598-020-60747-3)
| null | null |
One of tox21-spec-hepg2-p dataset which contains 6 different subdatasets.
|
Autofluoresence
| null | null | null | 210 |
High-Throughput Screening to Predict Chemical-Assay Interference
|
(Borrel, 2020, 10.1038/s41598-020-60747-3)
| null | null |
One of tox21-spec-hek293-p1 dataset which contains 6 different subdatasets.
|
Autofluoresence
| null | null | null | 56 |
High-Throughput Screening to Predict Chemical-Assay Interference
|
(Borrel, 2020, 10.1038/s41598-020-60747-3)
| null | null |
One of tox21-spec-hek293-p1 dataset which contains 6 different subdatasets.
|
Autofluoresence
| null | null | null | 33 |
High-Throughput Screening to Predict Chemical-Assay Interference
|
(Borrel, 2020, 10.1038/s41598-020-60747-3)
| null | null |
One of tox21-spec-hek293-p1 dataset which contains 6 different subdatasets.
|
Autofluoresence
| null | null | null | 224 |
High-Throughput Screening to Predict Chemical-Assay Interference
|
(Borrel, 2020, 10.1038/s41598-020-60747-3)
| null | null |
One of tox21-spec-hek293-p1 dataset which contains 6 different subdatasets.
|
Autofluoresence
| null | null | null | 44 |
High-Throughput Screening to Predict Chemical-Assay Interference
|
(Borrel, 2020, 10.1038/s41598-020-60747-3)
| null | null |
One of tox21-spec-hek293-p1 dataset which contains 6 different subdatasets.
|
Autofluoresence
| null | null | null | 34 |
High-Throughput Screening to Predict Chemical-Assay Interference
|
(Borrel, 2020, 10.1038/s41598-020-60747-3)
| null | null |
One of tox21-spec-hek293-p1 dataset which contains 6 different subdatasets.
|
ColloidalAggregators
| null | 585 | null | 1,256 |
SCAM Detective: Accurate Predictor of Small, Colloidally Aggregating Molecules
|
(Alves, 2020, 10.1021/acs.jcim.0c00415)
| null |
Promiscuous and Specific Inhibitors of AmpC Beta-Lactamase (assay without detergent)
| null |
ColloidalAggregators
| null | 1,476 | null | 4,500 |
Quantitative analyses of aggregation, autofluorescence, and reactivity artifacts in a screen for inhibitors of a thiol protease
|
(Jadhav, 2010, 10.1021/jm901070c)
| null |
qHTS Assay for Promiscuous and Specific Inhibitors of Cruzain (without detergent)
|
Inhibitors of Cruzain (without detergent)
|
ColloidalAggregators
| null | 485,341 | null | 1,730 |
SCAM Detective: Accurate Predictor of Small, Colloidally Aggregating Molecules
|
(Alves, 2020, 10.1021/acs.jcim.0c00415)
| null |
qHTS Inhibitors of AmpC Beta-Lactamase (assay without detergent)
|
qHTS Inhibitors of AmpC Beta-Lactamase (assay without detergent)
|
ColloidalAggregators
| null | null | null | 12,644 |
An Aggregation Advisor for Ligand Discovery
|
(Irwin, 2015, 10.1021/acs.jmedchem.5b01105)
| null | null |
20 diverse sources
|
HeavyHitters
| null | null | null | 13,068 |
Modeling Small-Molecule Reactivity Identifies Promiscuous Bioactive Compounds
|
(Matlock, 2018, 10.1021/acs.jcim.8b00104)
| null | null |
PubChem2016, non-ChEMBL tested in at least 100 assays
|
HeavyHitters
| null | null | null | 1,000 |
Statistical models for identifying frequent hitters in high throughput screening
|
(Goodwin, 2020, 10.1038/s41598-020-74139-0)
| null | null | null |
HeavyHitters
| null | null | null | 16,820 | null |
(Shi, 2024, 10.1093/nar/gkae424)
| null | null |
One of Shi2024 dataset which contains seven different subdatasets.
|
HeavyHitters
| null | null | null | 8,441 | null |
(Shi, 2024, 10.1093/nar/gkae424)
| null | null |
One of Shi2024 dataset which contains seven different subdatasets.
|
HeavyHitters
| null | null | null | 12,092 | null |
(Shi, 2024, 10.1093/nar/gkae424)
| null | null |
One of Shi2024 dataset which contains seven different subdatasets.
|
HeavyHitters
| null | null | null | 4,871 | null |
(Shi, 2024, 10.1093/nar/gkae424)
| null | null |
One of Shi2024 dataset which contains seven different subdatasets.
|
HeavyHitters
| null | null | null | 6,056 | null |
(Shi, 2024, 10.1093/nar/gkae424)
| null | null |
One of Shi2024 dataset which contains seven different subdatasets.
|
HeavyHitters
| null | null | null | 8,089 | null |
(Shi, 2024, 10.1093/nar/gkae424)
| null | null |
One of Shi2024 dataset which contains seven different subdatasets.
|
HeavyHitters
| null | null | null | 1,544 | null |
(Shi, 2024, 10.1093/nar/gkae424)
| null | null |
One of Shi2024 dataset which contains seven different subdatasets.
|
LuciferaseInhibition
| null | 411 | null | 1,571 |
Characterization of chemical libraries for luciferase inhibitory activity
|
(Auld, 2008, 10.1021/jm701302v)
| null |
qHTS Assay for Inhibitors of Firefly Luciferase
|
ATP concentration in the assay (10 uM)
|
LuciferaseInhibition
| null | 1,006 | null | 2,976 |
Characterization of chemical libraries for luciferase inhibitory activity
|
(Auld, 2008, 10.1021/jm701302v)
| null |
Counter Screen for Luciferase-based Primary Inhibition Assays
|
with >50% inhibition at 10 uM concentration
|
LuciferaseInhibition
| null | 1,379 | null | 565 |
A basis for reduced chemical library inhibition of firefly luciferase obtained from directed evolution
|
(Auld, 2009, 10.1021/jm8014525)
| null |
Counterscreen for Luciferase (Kinase-Glo TM) Inhibition
| null |
LuciferaseInhibition
| null | 1,891 | null | 446 | null | null | null |
Luminescence Biochemical Dose Response HTS to Identify Inhibitors of Luciferase
|
EC50 <= 1 log over the highest tested concentration
|
LuciferaseInhibition
| null | 2,515 | null | 10 | null | null | null |
Counterscreen Assay for Enhancers of SMN2 Splice Variant Expression: Interaction with Luciferase Reporter for Probe SAR
|
NIH Chemical Genomics Center (NCGC) Assay
|
LuciferaseInhibition
| null | 2,530 | null | 46 | null | null | null |
Secondary Assay for Luciferase (Kinase-Glo TM) Inhibition Counterscreen
| null |
LuciferaseInhibition
| null | 366,887 | null | 8 | null | null | null |
Inhibition of luciferin binding site of Photinus pyralis luciferase by noncompetitive inhibition assay
|
activity <= 10uM
|
LuciferaseInhibition
| null | 366,889 | null | 1 | null | null | null |
Inhibition of Photinus pyralis luciferase assessed as maximum inhibitory potency by quantitative high throughput screening
|
activity <= 10uM
|
LuciferaseInhibition
| null | 366,891 | null | 19 | null | null | null |
Inhibition of luciferin binding site of Photinus pyralis luciferase by competitive inhibition assay
|
activity <= 10uM
|
LuciferaseInhibition
| null | 488,838 | null | 41 | null | null | null |
Counterscreen Assay for Enhancers of SMN2 Splice Variant Expression: Interaction with Luciferase Reporter for Further Probe SAR
| null |
LuciferaseInhibition
| null | 493,175 | null | 0 | null | null | null |
miR-21 counterscreen using purified firefly luciferase
| null |
LuciferaseInhibition
| null | 588,342 | null | 25,069 |
Firefly luciferase in chemical biology: a compendium ofinhibitors, mechanistic evaluation of chemotypes, and suggested use as areporter.
|
(Thorne, 2012, 10.1016/j.chembiol.2012.07.015)
| null |
qHTS profiling assay for firefly luciferase inhibitor/activator using purifed enzyme and Km concentrations of substrates (counterscreen for miR-21 project)
| null |
LuciferaseInhibition
| null | 588,498 | null | 18 | null | null | null |
qHTS profiling assay for firefly luciferase inhibitor/activator using purifed enzyme and Km concentrations of substrates (counterscreen for the Campaign to Identify EBNA-1 Inhibitors project).
| null |
LuciferaseInhibition
| null | 602,357 | null | 104 | null | null | null |
FLuc inhibitory activity for the follow-up compounds in a biochemical assay with Km concentrations of substrate
| null |
LuciferaseInhibition
| null | 602,358 | null | 102 | null | null | null |
FLuc inhibitory activity for the follow-up compounds in a biochemical assay with Km concentrations of substrate and 500microM CoASH
| null |
LuciferaseInhibition
| null | 602,364 | null | 89 | null | null | null |
FLuc inhibitory activity for the follow-up compounds in a biochemical assay with 1mM ATP
| null |
LuciferaseInhibition
| null | 602,474 | null | 53 | null | null | null |
FLuc inhibitory activity for the follow-up compounds in a biochemical assay with a commercial detection reagent - BriteliteTM Plus (PerkinElmer)
| null |
LuciferaseInhibition
| null | 602,475 | null | 36 | null | null | null |
FLuc inhibitory activity for the follow-up compounds in a biochemical assay with an in-house formulation of detection reagent
| null |
LuciferaseInhibition
| null | 602,476 | null | 31 | null | null | null |
FLuc inhibitory activity for the follow-up compounds in a cell-based translational read-through assay
| null |
LuciferaseInhibition
| null | 602,477 | null | 87 | null | null | null |
FLuc inhibitory activity for the follow-up compounds in a cell-based assay to assess the activity of miR-21
| null |
LuciferaseInhibition
| null | 624,030 | null | 104 | null | null | null |
Biochemical firefly luciferase enzyme assay for NPC
| null |
LuciferaseInhibition
| null | 652,016 | null | 35 | null | null | null |
qHTS Assay for Inhibitors of Firefly Luciferase from the GSK Published Protein Kinase Inhibitor Set
| null |
End of preview.
ChAFF datasets
This dataset collection contains ~200K curated Active compound lists from ~90 different BioAssay datasets, focusing on known assay interference artifacts. We applied SMILES standardization using RDKit and MolVS, including molecule sanitization and fragment removal. The final dataset is suitable for training and evaluating machine learning models.
Types and Number of Active Compounds
| Type | NumActiveCompounds |
|---|---|
| Absorbance | 1486 |
| Artifact | 10952 |
| Autofluoresence | 32054 |
| ColloidalAggregators | 19553 |
| HeavyHitters | 71981 |
| LuciferaseInhibition | 32831 |
| Misannotation | 39 |
| Reactivity | 3107 |
| REDOX | 217 |
Dataset Columns
| Column | Description |
|---|---|
| Type | Task domain (e.g. Absorbance) |
| DatasetName | Source dataset name |
| AID | Pubchem Assay ID |
| ID | Identifier for the compound |
| IDType | Type of identifier (e.g. CID) |
| SMILES | Curated SMILES |
Datasets can be found in the data folder.
Dataset summary
A summary file is uploaded, which lists:
- Type
- DatasetName
- AID
- NumActiveCompounds
- PaperTitle
- Reference
- URL
- AssayName
- Description
Dataset summary file can be found: ChAFF_dataset_summary.json
License
Each dataset comes from different sources (i.e., PubChem, Papers). Please check our dataset summary file if you are looking for references.
Usage
Load a dataset in python
Each subset can be loaded into python using the Huggingface datasets library.
First, from the command line install the datasets library
$ pip install datasets
then, from within python load the datasets library.
>>> import datasets
>>> from datasets import load_dataset, Features, Value
Specifiy column types to prevent pyarrow error.
features = Features({
"Type": Value("string"),
"DatasetName": Value("string"),
"AID": Value("string"), # Treat int as string
"ID": Value("string"),
"IDType": Value("string"),
"SMILES": Value("string")
})
Now load one of the 'ChAFF' datasets, e.g.,
>>> dataset = datasets.load_dataset("maomlab/ChAFF", name = "default", data_files = "data/Absorbance.csv", split = "train", features = features)
You can modify "data/Absorbance.csv" based on your interest (e.g., "data/Reactivity.csv"). The default is split = "train" as we did not split the datasets.
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