> For the complete documentation index, see [llms.txt](https://ersilia.gitbook.io/ersilia-book/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://ersilia.gitbook.io/ersilia-book/chemistry-tools/automated-activity-prediction-models.md).

# Automated activity prediction models

Quick baseline modeling of chemistry data can be done with [LazyQSAR](https://github.com/ersilia-os/lazy-qsar), our fast modeling tool. LazyQSAR produces light-weight models for binary classification and regression tasks.

{% content-ref url="/pages/4E5BcGvhl9TkLM4X9Voa" %}
[Light-weight AutoML with LazyQSAR](/ersilia-book/chemistry-tools/automated-activity-prediction-models/light-weight-automl-with-lazyqsar.md)
{% endcontent-ref %}

Our flagship AutoML tool for chemistry is [ZairaChem](https://github.com/ersilia-os/zaira-chem). This Python library offers robust ensemble-based modeling capabilities applicable to a wide range of modeling scenarios. At the moment, ZairaChem is focused on binary classification and regression tasks.

{% content-ref url="<https://github.com/ersilia-os/ersilia-book/blob/main/book/chemistry-tools/automated-activity-prediction-models/accurate-automl-with-zairachem.md>" %}
<https://github.com/ersilia-os/ersilia-book/blob/main/book/chemistry-tools/automated-activity-prediction-models/accurate-automl-with-zairachem.md>
{% endcontent-ref %}

In addition, we have developed a model distillation pipeline named [Olinda](https://github.com/ersilia-os/olinda) aimed at producing light, interoperable models in [ONNX](https://onnx.ai/) format.

{% content-ref url="/pages/esWLDoGqttGtrDgt8fdh" %}
[Model distillation with Olinda](/ersilia-book/chemistry-tools/automated-activity-prediction-models/model-distillation-with-olinda.md)
{% endcontent-ref %}