Datasets:
metadata
license: cc-by-4.0
tags:
- chemistry
- biology
pretty_name: >-
CatPred A comprehensive framework for deep learning in vitro enzyme kinetic
parameters
repo: https://github.com/maranasgroup/CatPred-DB
citation_bibtex: >-
@article{Boorla2025,title = {CatPred: a comprehensive framework for deep
learning in vitro enzyme kinetic parameters},volume = {16},ISSN =
{2041-1723},url = {http://dx.doi.org/10.1038/s41467-025-57215-9},DOI =
{10.1038/s41467-025-57215-9},number = {1},journal = {Nature
Communications},publisher = {Springer Science and Business Media LLC},author =
{Boorla, Veda Sheersh and Maranas, Costas D.},year = {2025},month = feb}
citation_apa: >-
Boorla, V. S., & Maranas, C. D. (2025). CatPred: a comprehensive framework for
deep learning in vitro enzyme kinetic parameters. Nature Communications,
16(1), 2072. doi:10.1038/s41467-025-57215-9
configs:
- config_name: kcat
data_files:
- split: train
path: kcat/kcat_train.csv
- split: test
path: kcat/kcat_test.csv
- split: val
path: kcat/kcat_val.csv
- config_name: ki
data_files:
- split: train
path: ki/ki_train.csv
- split: test
path: ki/ki_test.csv
- split: val
path: ki/ki_val.csv
- config_name: km
data_files:
- split: train
path: km/km_train.csv
- split: test
path: km/km_test.csv
- split: val
path: km/km_val.csv
dataset_info:
- config_name: kcat
features:
- name: sequence
dtype: string
- name: sequence_source
dtype: string
- name: uniprot
dtype: string
- name: reaction_smiles
dtype: string
- name: value
dtype: float64
- name: reaction_mw_diff_perc
dtype: float64
- name: temperature
dtype: float64
- name: ph
dtype: float64
- name: ec
dtype: string
- name: taxonomy_id
dtype: float64
- name: log10_value
dtype: float64
- name: reactant_smiles
dtype: string
- name: product_smiles
dtype: string
- name: log10kcat_max
dtype: float64
- name: group
dtype: string
- name: pdbpath
dtype: string
- name: reactant_smiles_20cluster
dtype: int64
- name: sequence_20cluster
dtype: int64
- name: reactant_smiles_40cluster
dtype: int64
- name: sequence_40cluster
dtype: int64
- name: reactant_smiles_60cluster
dtype: int64
- name: sequence_60cluster
dtype: int64
- name: reactant_smiles_80cluster
dtype: int64
- name: sequence_80cluster
dtype: int64
- name: reactant_smiles_99cluster
dtype: int64
- name: sequence_99cluster
dtype: int64
- config_name: km
features:
- name: sequence
dtype: string
- name: sequence_source
dtype: string
- name: uniprot
dtype: string
- name: substrate_smiles
dtype: string
- name: value
dtype: float64
- name: temperature
dtype: float64
- name: ph
dtype: float64
- name: ec
dtype: string
- name: taxonomy_id
dtype: float64
- name: log10_value
dtype: float64
- name: log10km_mean
dtype: float64
- name: group
dtype: string
- name: pdbpath
dtype: string
- name: substrate_smiles_20cluster
dtype: int64
- name: sequence_20cluster
dtype: int64
- name: substrate_smiles_40cluster
dtype: int64
- name: sequence_40cluster
dtype: int64
- name: substrate_smiles_60cluster
dtype: int64
- name: sequence_60cluster
dtype: int64
- name: substrate_smiles_80cluster
dtype: int64
- name: sequence_80cluster
dtype: int64
- name: substrate_smiles_99cluster
dtype: int64
- name: sequence_99cluster
dtype: int64
- config_name: ki
features:
- name: sequence
dtype: string
- name: sequence_source
dtype: string
- name: uniprot
dtype: string
- name: substrate_smiles
dtype: string
- name: value
dtype: float64
- name: temperature
dtype: float64
- name: ph
dtype: float64
- name: ec
dtype: string
- name: taxonomy_id
dtype: float64
- name: log10_value
dtype: float64
- name: log10ki_mean
dtype: float64
- name: group
dtype: string
- name: pdbpath
dtype: string
- name: substrate_smiles_20cluster
dtype: int64
- name: sequence_20cluster
dtype: int64
- name: substrate_smiles_40cluster
dtype: int64
- name: sequence_40cluster
dtype: int64
- name: substrate_smiles_60cluster
dtype: int64
- name: sequence_60cluster
dtype: int64
- name: substrate_smiles_80cluster
dtype: int64
- name: sequence_80cluster
dtype: int64
- name: substrate_smiles_99cluster
dtype: int64
- name: sequence_99cluster
dtype: int64
- name: canonical_smiles
dtype: string
CatPred: A comprehensive framework for deep learning in vitro enzyme kinetic parameters
CatPred-DB is a curated benchmark dataset for in vitro enzyme kinetic parameters, compiled from the BRENDA and SABIO-RK databases.
It covers three key kinetic measurements:
kcat (~23k data points) turnover number, how fast an enzyme converts substrate to product
Km (~41k data points) Michaelis constant, substrate concentration at half-max enzyme activity
Ki (~12k data points) inhibition constant, how potently a molecule inhibits an enzyme
Quickstat Usage
Install HuggingFace Datasets package
Each subset can be loaded into python using the Huggingface datasets 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 CatPred model datasets (see available datasets below), use datasets.load_dataset(...):
>>> dataset_tag = "km"
>>> km = datasets.load_dataset(
path = "mcguire1/RconEasyDataset",
name = dataset_tag,
data_dir = dataset_tag)
Generating train split: 33350 examples [00:00, 79921.22 examples/s]
Generating validation split: 3706 examples [00:00, 90060.55 examples/s]
Generating test split: 4118 examples [00:00, 98110.42 examples/s]
and the dataset is loaded as a datasets.arrow_dataset.Dataset
>>> km
DatasetDict({
train: Dataset({
features: ['sequence', 'sequence_source', 'uniprot', 'substrate_smiles', 'value', 'temperature', 'ph', 'ec', 'taxonomy_id', 'log10_value', 'log10km_mean', 'group', 'pdbpath', 'substrate_smiles_20cluster', 'sequence_20cluster', 'substrate_smiles_40cluster', 'sequence_40cluster', 'substrate_smiles_60cluster', 'sequence_60cluster', 'substrate_smiles_80cluster', 'sequence_80cluster', 'substrate_smiles_99cluster', 'sequence_99cluster'],
num_rows: 33350
})
validation: Dataset({
features: ['sequence', 'sequence_source', 'uniprot', 'substrate_smiles', 'value', 'temperature', 'ph', 'ec', 'taxonomy_id', 'log10_value', 'log10km_mean', 'group', 'pdbpath', 'substrate_smiles_20cluster', 'sequence_20cluster', 'substrate_smiles_40cluster', 'sequence_40cluster', 'substrate_smiles_60cluster', 'sequence_60cluster', 'substrate_smiles_80cluster', 'sequence_80cluster', 'substrate_smiles_99cluster', 'sequence_99cluster'],
num_rows: 3706
})
test: Dataset({
features: ['sequence', 'sequence_source', 'uniprot', 'substrate_smiles', 'value', 'temperature', 'ph', 'ec', 'taxonomy_id', 'log10_value', 'log10km_mean', 'group', 'pdbpath', 'substrate_smiles_20cluster', 'sequence_20cluster', 'substrate_smiles_40cluster', 'sequence_40cluster', 'substrate_smiles_60cluster', 'sequence_60cluster', 'substrate_smiles_80cluster', 'sequence_80cluster', 'substrate_smiles_99cluster', 'sequence_99cluster'],
num_rows: 4118
})
})
which is a column oriented format that can be accessed directly, written to disk as a parquet file or converted in to a pandas.DataFrame, e.g.
>>> km['train'].data.column('sequence')
<pyarrow.lib.ChunkedArray object at 0x35fda2260>