Welcome to OpenADMET Models documentation!
The OpenADMET Models package provides a suite of FOSS tools for building, training, and evaluating machine learning models on chemical matter. It includes a variety of featurizers, model architectures, and evaluation metrics to facilitate the development of robust and accurate predictive models, with a particular focus on ADMET properties.
The library includes traditional machine learning methods, deep learning models, and active learning workflows. It is designed for general-purpose use and is not intended to implement every state-of-the-art architecture, but rather to provide a practical, flexible foundation for ADMET modeling.
This documentation provides an overview of the package’s features, as well as detailed guides on how to use the various components. See the sections below to get started.
Useful Links: OpenADMET Website | Example Tutorial Notebooks | Source Repository | Issues & Ideas
Instructions for installing OpenADMET Models.
New to OpenADMET Models? Try it out in your browser with Google Colab (may take a moment to load).
A series of step-by-step tutorials demonstrating key features.
How to use the API of OpenADMET Models.
How to contribute to OpenADMET Models.
Reference guide to Anvil workflows within OpenADMET Models.