README.md

---
title: Tox21 SNN Classifier
emoji: 🌖
colorFrom: green
colorTo: pink
sdk: docker
pinned: false
license: cc-by-nc-4.0
short_description: Self-Normalizing Neural Network Baseline for Tox21
---

# Tox21 SNN Classifier

This repository hosts a Hugging Face Space that provides an examplary API for submitting models to the [Tox21 Leaderboard](https://huggingface.co/spaces/tschouis/tox21_leaderboard).

In this example, we train a XGBoost classifier on the Tox21 targets and save the trained model in the `assets/` folder.

**Important:** For leaderboard submission, your Space does not need to include training code. It only needs to implement inference in the `predict()` function inside `predict.py`. The `predict()` function must keep the provided skeleton: it should take a list of SMILES strings as input and return a prediction dictionary as output, with SMILES and targets as keys. Therefore, any preprocessing of SMILES strings must be executed on-the-fly during inference.

# Repository Structure
- `predict.py` - Defines the `predict()` function required by the leaderboard (entry point for inference).
- `app.py` - FastAPI application wrapper (can be used as-is).

- `src/` - Core model & preprocessing logic:
    - `data.py` - SMILES preprocessing pipeline
    - `model.py` - XGBoost classifier wrapper
    - `train.py` - Script to train the classifier
    - `utils.py` – Constants and Helper functions

# Quickstart with Spaces

You can easily adapt this project in your own Hugging Face account:

- Open this Space on Hugging Face.

- Click "Duplicate this Space" (top-right corner).

- Modify `src/` for your preprocessing pipeline and model class

- Modify `predict()` inside `predict.py` to perform model inference while keeping the function skeleton unchanged to remain compatible with the leaderboard.

That’s it, your model will be available as an API endpoint for the Tox21 Leaderboard.

# Installation
To run (and train) the XGBoost, clone the repository and install dependencies:

```bash
git clone https://huggingface.co/spaces/tschouis/tox21_snn_classifier
cd tox21_snn_classifier

conda create -n tox21_snn_cls python=3.11
conda activate tox21_snn_cls
pip install -r requirements.txt
```

# Training

To train the XGBoost model from scratch:

```bash
python -m src/train.py
```

This will:

1. Load and preprocess the Tox21 training dataset.
2. Train a XGBoost classifier.
3. Save the trained model to the assets/ folder.
4. Evaluate the trained XGBoost classifier on the validation split. 


# Inference

For inference, you only need `predict.py`.

Example usage inside Python:

```python
from predict import predict

smiles_list = ["CCO", "c1ccccc1", "CC(=O)O"]
results = predict(smiles_list)

print(results)
```

The output will be a nested dictionary in the format:

```python
{
    "CCO": {"target1": 0, "target2": 1, ..., "target12": 0},
    "c1ccccc1": {"target1": 1, "target2": 0, ..., "target12": 1},
    "CC(=O)O": {"target1": 0, "target2": 0, ..., "target12": 0}
}
```

# Notes

- Only adapting `predict.py` for your model inference is required for leaderboard submission.

- Training (`src/train.py`) is provided for reproducibility.

- Preprocessing (here inside `src/data.py`) must be applied at inference time, not just training.