| --- |
| 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](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 `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> |
| |
| ## Overview of Datasets |
| Benchmark datasets for enzyme kinetic parameters. Contains 23k, 41k, 12k data points for Kcat, Km, and Ki, respectively derived from experimental assays. |
|
|
|
|
| ## Citation |
| 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 |
|
|
