# Light-weight AutoML with LazyQSAR

## Installation

```bash
git clone https://github.com/ersilia-os/lazy-qsar.git
cd lazy-qsar
python -m pip install -e .
```

## Usage

### Get an example

You can find example data in the fantastic [Therapeutic Data Commons](https://tdcommons.ai/) portal.

```python
from tdc.single_pred import Tox
data = Tox(name = 'hERG')
split = data.get_split()
```

Here we are selecting the hERG blockade toxicity dataset. Let's refactor data for convenience.

```python
smiles_train = list(split["train"]["Drug"])
y_train = list(split["train"]["Y"])
smiles_valid = list(split["valid"]["Drug"])
y_valid = list(split["valid"]["Y"])
```

### Build a model

Now we can train (and validate) a model based on Morgan fingerprints.

```python
import lazyqsar as lq

# train
model = lq.MorganBinaryClassifier()
model.fit(smiles_train, y_train)

# validate
from sklearn.metrics import roc_curve, auc
y_hat = model.predict_proba(smiles_valid)[:,1]
fpr, tpr, _ = roc_curve(y_valid, y_hat)
print("AUROC", auc(fpr, tpr))
```

{% hint style="warning" %}
This library is only intended for quick-and-dirty QSAR modeling. For a more complete automated QSAR modeling, please refer to [ZairaChem](https://github.com/ersilia-os/zaira-chem)
{% endhint %}


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://ersilia.gitbook.io/ersilia-book/chemistry-tools/automated-activity-prediction-models/light-weight-automl-with-lazyqsar.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.