initial commit

.gitignore

@@ -0,0 +1,6 @@
+tox21_test.csv
+results.csv
+predict copy.py
+debug.py
+__pycache__
+tox21_test.csv

Dockerfile

@@ -0,0 +1,16 @@
+# Read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
+# you will also find guides on how best to write your Dockerfile
+
+FROM python:3.11.4
+
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+
+WORKDIR /app
+
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+COPY --chown=user . /app
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

@@ -1,12 +1,82 @@
 ---
 title: Tox21 Chemprop Classifier
-emoji: 🏃
-colorFrom: gray
+emoji: 🤖
+colorFrom: yellow
 colorTo: pink
 sdk: docker
 pinned: false
 license: mit
-short_description: Chemprop Baseline Classifier for Tox21
+short_description: Chemprop Message Passing Neural Network Baseline Classifier for Tox21
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Tox21 Chemprop 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 trained a Chemprop classifier on the Tox21 targets and saved 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 nested prediction dictionary as output, with SMILES as keys and dictionaries containing targetname-prediction pairs as values. 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:
+    - `preprocess.py` - SMILES preprocessing pipeline
+    - `model.py` - GIN classifier
+    - `seed.py` - used to ensure reproducibility 
+
+# 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 the Chemprop classifier, clone the repository and install dependencies:
+
+```bash
+git clone https://huggingface.co/spaces/tschouis/tox21_chemprop_classifier
+cd tox21_chemprop_classifier
+pip install -r requirements.txt
+```
+
+
+# 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.
+
+- Preprocessing (here inside `src/preprocess.py`) must be applied at inference time, not just predicting.

app.py

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+"""
+This is the main entry point for the FastAPI application.
+The app handles the request to predict toxicity for a list of SMILES strings.
+"""
+
+# ---------------------------------------------------------------------------------------
+# Dependencies and global variable definition
+import os
+from typing import List, Dict, Optional
+from fastapi import FastAPI, Header, HTTPException
+from pydantic import BaseModel, Field
+
+from predict import predict as predict_func
+
+API_KEY = os.getenv("API_KEY")  # set via Space Secrets
+
+
+# ---------------------------------------------------------------------------------------
+class Request(BaseModel):
+    smiles: List[str] = Field(min_items=1, max_items=1000)
+
+
+class Response(BaseModel):
+    predictions: dict
+    model_info: Dict[str, str] = {}
+
+
+app = FastAPI(title="toxicity-api")
+
+
+@app.get("/")
+def root():
+    return {
+        "message": "Toxicity Prediction API",
+        "endpoints": {
+            "/metadata": "GET - API metadata and capabilities",
+            "/healthz": "GET - Health check",
+            "/predict": "POST - Predict toxicity for SMILES",
+        },
+        "usage": "Send POST to /predict with {'smiles': ['your_smiles_here']} and Authorization header",
+    }
+
+
+@app.get("/metadata")
+def metadata():
+    return {
+        "name": "AwesomeTox",
+        "version": "1.0.0",
+        "max_batch_size": 256,
+        "tox_endpoints": [
+            "NR-AR",
+            "NR-AR-LBD",
+            "NR-AhR",
+            "NR-Aromatase",
+            "NR-ER",
+            "NR-ER-LBD",
+            "NR-PPAR-gamma",
+            "SR-ARE",
+            "SR-ATAD5",
+            "SR-HSE",
+            "SR-MMP",
+            "SR-p53",
+        ],
+    }
+
+
+@app.get("/healthz")
+def healthz():
+    return {"ok": True}
+
+
+@app.post("/predict", response_model=Response)
+def predict(request: Request):
+    predictions = predict_func(request.smiles)
+    return {
+        "predictions": predictions,
+        "model_info": {"name": "random_clf", "version": "1.0.0"},
+    }

assets/best1.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ad0ce975384e5c0aec213af7171a4a290bbf0aa4070d74c891ae9d01af3fbe75
+size 2844412

predict.py

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+from torch_geometric.data import Batch
+from torch_geometric.utils import from_rdmol
+import torch
+import csv
+import subprocess
+import pandas as pd
+
+from src.preprocess import create_clean_smiles
+
+def predict(smiles_list):
+    """
+    Predict toxicity targets for a list of SMILES strings.
+
+    Args:
+        smiles_list (list[str]): SMILES strings
+
+    Returns:
+        dict: {smiles: {target_name: prediction_prob}}
+    """
+    # clean smiles
+    clean_smiles, valid_mask = create_clean_smiles(smiles_list)
+
+    # tox21 targets
+    TARGET_NAMES = [
+            "NR-AhR","NR-AR","NR-AR-LBD","NR-Aromatase","NR-ER","NR-ER-LBD","NR-PPAR-gamma","SR-ARE","SR-ATAD5","SR-HSE","SR-MMP","SR-p53"
+        ]
+    DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print(f"Received {len(smiles_list)} SMILES strings")
+
+    # put smiles into csv
+    with open("./src/smiles.csv", "w", newline="") as f:
+        writer = csv.writer(f)
+        writer.writerow(["smiles"])  # header
+        for smi in clean_smiles:
+            writer.writerow([smi])
+
+    # predict
+    command = [
+    "chemprop", "predict",
+    "--test-path", "src/smiles.csv",
+    "--model-path", "assets/best1.pt",
+    "--smiles-columns", "smiles",
+    "--preds-path", "src/preds.csv"
+]
+
+    # Run the command
+    subprocess.run(command, check=True)
+
+    # create results dictionary from predictions
+
+    csv_path = "./src/preds.csv"
+
+    predictions = {}
+
+    # Read the CSV
+    with open(csv_path, "r", newline="") as f:
+        reader = csv.DictReader(f)
+        target_names = [col for col in reader.fieldnames if col != "smiles"]
+
+        for row in reader:
+            smiles = row["smiles"]
+            # Create a dict with target: float(pred)
+            pred_dict = {t: float(row[t]) for t in target_names}
+            predictions[smiles] = pred_dict
+
+    # add placeholders (0.5) for invalid smiles
+    for smi, is_valid in zip(smiles_list, valid_mask):
+        if not is_valid:
+            # if not valid, assign 0.5 for all targets
+            predictions[smi] = {t: 0.5 for t in TARGET_NAMES}
+
+    return predictions

requirements.txt

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+fastapi
+uvicorn[standard]
+torch==2.3.0
+torch-geometric==2.6.1
+numpy==1.26.2
+pandas==2.2.2
+rdkit==2024.3.6
+pydantic
+typing-extensions

src/preprocess.py

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+import numpy as np
+
+from rdkit import Chem
+from rdkit.Chem.MolStandardize import rdMolStandardize
+from rdkit import Chem
+import numpy as np
+
+def create_clean_smiles(smiles_list: list[str]) -> tuple[list[str], np.ndarray]:
+    """
+    Clean and canonicalize SMILES strings while staying in SMILES space.
+    Returns (list of cleaned SMILES, mask of valid SMILES).
+    """
+    clean_smis = []
+    valid_mask = []
+
+    cleaner = rdMolStandardize.CleanupParameters()
+    tautomer_enumerator = rdMolStandardize.TautomerEnumerator()
+
+    for smi in smiles_list:
+        try:
+            mol = Chem.MolFromSmiles(smi)
+            if mol is None:
+                valid_mask.append(False)
+                continue
+
+            # Cleanup and tautomer canonicalization
+            mol = rdMolStandardize.Cleanup(mol, cleaner)
+            mol = tautomer_enumerator.Canonicalize(mol)
+
+            # Canonical SMILES output
+            clean_smi = Chem.MolToSmiles(mol, canonical=True)
+            clean_smis.append(clean_smi)
+            valid_mask.append(True)
+
+        except Exception as e:
+            print(f"Failed to clean {smi}: {e}")
+            valid_mask.append(False)
+
+    return clean_smis, np.array(valid_mask, dtype=bool)

src/smiles.csv

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