README.md

---
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-DB: Enzyme Kinetic Parameters Database

- **Paper:** [CatPred: A comprehensive framework for deep learning in vitro enzyme kinetic parameters](https://www.nature.com/articles/s41467-025-57215-9)
- **GitHub:** https://github.com/maranasgroup/CatPred-DB

## Dataset Description

CatPred-DB contains the benchmark datasets introduced alongside the CatPred deep learning framework for predicting in vitro enzyme kinetic parameters. The datasets cover three key kinetic parameters:

| Parameter | Description | Datapoints |
| --- | --- | --- |
| *k*cat | Turnover number | 23,197 |
| *K*m | Michaelis constant | 41,174 |
| *K*i | Inhibition constant | 11,929 |

These datasets were curated to address the lack of standardized, high-quality benchmarks for enzyme kinetics prediction, with particular attention to coverage of out-of-distribution enzyme sequences.

---

## Uses

**Direct Use:** This dataset is intended for training, evaluating, and benchmarking machine learning models that predict enzyme kinetic parameters from protein sequences or structural features.

**Downstream Use:** The dataset can be used to train or benchmark other machine learning models for enzyme kinetic parameter prediction, or to reproduce and extend the experiments described in the CatPred publication.

**Out-of-Scope Use:** This dataset reflects *in vitro* measurements and may not generalize to *in vivo* conditions. It should not be used as a sole basis for clinical or industrial enzyme selection without additional experimental validation.

---

## Dataset Structure
The repository contains:
- datasets/ – CSV files for *k*cat, *K*m, and *K*i with train/test splits
- scripts/ – Preprocessing and utility scripts

---

## Data Fields

Each entry typically includes:
| Field | Description |
|---|---|
| `sequence` | Enzyme amino acid sequence |
| `sequence_source` | Source of the sequence |
| `uniprot` | UniProt identifier |
| `substrate_smiles` | Substrate chemical structure in SMILES format |
| `value` | Raw measured kinetic parameter value |
| `log10_value` | Log10-transformed kinetic value (use this for modeling) |
| `log10km_mean` | Log10 mean Km value for the enzyme-substrate pair |
| `temperature` | Assay temperature (°C) |
| `ph` | Assay pH |
| `ec` | Enzyme Commission (EC) number |
| `taxonomy_id` | NCBI taxonomy ID of the source organism |
| `group` | Train/val/test split assignment |
| `pdbpath` | Path to associated PDB structural file (if available) |
| `sequence_40cluster` | Sequence cluster ID at 40% identity threshold |
| `sequence_60cluster` | Sequence cluster ID at 60% identity threshold |
| `sequence_80cluster` | Sequence cluster ID at 80% identity threshold |
| `sequence_99cluster` | Sequence cluster ID at 99% identity threshold |

---

## Source Data

Data was compiled and curated from public biochemical databases, including BRENDA and SABIO-RK, as described in the CatPred publication. Splits were designed to evaluate generalization to enzyme sequences dissimilar to those seen during training.
All SMILES were sanitized with RdKit. Broken SMILES were removed.

---

## Dataset Splits
Each kinetic parameter (kcat, km, ki) has two split strategies, described below.

### Split strategies

**Random splits** divide the data without regard to sequence similarity. These are useful
for a general baseline but tend to overestimate real-world model performance, since
training and test enzymes may be closely related.

**Sequence-similarity splits** (`seq_test_sequence_XXcluster`) ensure that test set enzymes
share less than XX% sequence identity with any enzyme in the training set. This is the
more rigorous benchmark — a model that performs well here is genuinely generalizing to
novel enzymes rather than recognizing similar sequences it has effectively seen before.

Five strictness levels are provided:
| Cluster threshold | Test set stringency |
| --- | --- |
| 20% | Hardest — test enzymes are very dissimilar to training data |
| 40% | Hard |
| 60% | Moderate |
| 80% | Easy |
| 99% | Easiest — nearly any sequence may appear in test |

### File Naming

Each split file is named `{parameter}-{strategy}_{subset}.csv`. The subsets are:

| Subset | Contents | When to use |
|---|---|---|
| `train` | Training data only | Model development and hyperparameter tuning |
| `val` | Validation data only | Monitoring training, early stopping |
| `test` | Test data only | Final benchmark evaluation |
| `trainval` | Train + val combined | Retrain final model after hyperparameters are locked in |
| `trainvaltest` | All data combined | Train a release model once all evaluation is complete |


---

## Quickstart 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

```bash
>>> pip install datasets
```

### Load a subset

```python
>>> from datasets import load_dataset

# Options: "kcat", "km", "ki"
>>> ds = load_dataset("RosettaCommons/CatPred-DB", "kcat")

>>> train = ds["train"]
>>> val   = ds["validation"]
>>> test  = ds["test"]
```

```
kcat-random_train.csv: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 28.0M/28.0M [00:04<00:00, 6.29MB/s]
kcat-random_trainval.csv: 100%|██████████████████████████████████████████████████████████████████████████████████████████| 31.1M/31.1M [00:04<00:00, 6.81MB/s]
kcat-random_trainvaltest.csv: 100%|██████████████████████████████████████████████████████████████████████████████████████| 34.5M/34.5M [00:05<00:00, 6.86MB/s]
Generating train split: 100%|█████████████████████████████████████████████████████████████████████████████████| 18789/18789 [00:00<00:00, 67580.51 examples/s]
Generating validation split: 100%|████████████████████████████████████████████████████████████████████████████| 20877/20877 [00:00<00:00, 78951.22 examples/s]
Generating test split: 100%|██████████████████████████████████████████████████████████████████████████████████| 23197/23197 [00:00<00:00, 74253.91 examples/s]
```

### Key columns

| Column | Description |
| --- | --- |
| `sequence` | Enzyme amino acid sequence |
| `uniprot` | UniProt identifier |
| `reactant_smiles` | Substrate SMILES |
| `value` | Raw kinetic value |
| `log10_value` | Log₁₀-transformed value — **use this as your target** |
| `temperature` | Assay temperature (°C), nullable |
| `ph` | Assay pH, nullable |
| `ec` | EC number |
| `sequence_40cluster` | Cluster ID at 40% identity — use for similarity-based splits |

### Recommended split workflow

```
train + val         →   tune architecture and hyperparameters
trainval + test     →   final benchmark (report results here)
trainvaltest        →   train the final released model on all available data
```

This three-stage approach is standard practice in ML: you only touch the test set once,
and the combined files make it easy to retrain on progressively more data as you move
from experimentation to deployment.

### Basic training setup
```python
>>> df = ds["train"].to_pandas()

>>> X_seq = df["sequence"]
>>> X_sub = df["reactant_smiles"]
>>> y     = df["log10_value"]

# Drop rows with missing targets or substrates
>>> mask = y.notna() & X_sub.notna()
>>> df   = df[mask]
```

---

## Citation

If you use this dataset, please cite:

**BibTeX:**
```bibtex
@article{boorla2025catpred,
  title={CatPred: a comprehensive framework for deep learning in vitro enzyme kinetic parameters},
  author={Boorla, Veda Sheersh and Maranas, Costas D.},
  journal={Nature Communications},
  volume={16},
  pages={2072},
  year={2025},
  doi={10.1038/s41467-025-57215-9}
}
```

**APA:**
Boorla, V. S., & Maranas, C. D. (2025). CatPred: a comprehensive framework for deep learning in vitro enzyme kinetic parameters. *Nature Communications*, 16, 2072. https://doi.org/10.1038/s41467-025-57215-9

## License

MIT - see [LICENSE](https://github.com/maranasgroup/CatPred-DB/blob/main/LICENSE)

---

## Dataset Card Authors
Jessica Lin, Kuniko Hunter, Manasa Yadavalli, McGuire Metts