--- 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') ## Overview of Datasets