README.md

---
language: en
license: mit
size_categories:
- 100k<n<1M
pretty_name: 'Enzyme Catalysis Database'
tags:
- enzymes
- kinetics
- biochemistry 
- deep-learning
- protein
configs:
  - config_name: kcat
    data_files:
      - split: train
        path: datasets/splits/kcat-random_train.csv
      - split: validation
        path: datasets/splits/kcat-random_trainval.csv
      - split: test
        path: datasets/splits/kcat-random_trainvaltest.csv
  - config_name: ki
    data_files:
      - split: train
        path: datasets/splits/ki-random_train.csv
      - split: validation
        path: datasets/splits/ki-random_trainval.csv
      - split: test
        path: datasets/splits/ki-random_trainvaltest.csv
  - config_name: km
    data_files:
      - split: train
        path: datasets/splits/km-random_train.csv
      - split: validation
        path: datasets/splits/km-random_trainval.csv
      - split: test
        path: datasets/splits/km-random_trainvaltest.csv
dataset_summary: >- 
  A large-scale enzyme kinetics database containing experimentally measured kinetic parameters paired with enzyme sequences and substrate SMILES. It's designed for training and benchmarking deep learning models that predict enzyme catalytic activity.
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: Kcat, Km, Ki.
acknowledgements: >- 
  We kindly acknowledge the support of the Rosetta Commons Data Bazaar organizers. 
repo: https://github.com/maranasgroup/CatPred-DB
citation_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}
---

# 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.

---

## 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("kunikohunter/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)