Community contributions

We love community contributions!

This page is to celebrate and showcase the contributors that have gone above and beyond!

Is your work not listed here?

We do our best to list all the work we are aware of, but if we missed your contribution feel free to open a Github issue to let us know! We will add it as soon as possible.

Plugins

Plugins add new featurizers to the molfeat ecosystem by extending its functionality with plug-and-play components. To learn more, see Extending molfeat.

Link	Name	Author	Description
	molfeat-padel	@datamol.io	Adds support for the PaDeL descriptors, as introduced by Yap, 2010. This is the official exemplary plugin for molfeat.
	molfeat-hype	@maclandrol	Investigates the performance of embeddings from various LLMs trained without explicit molecular context for molecular modeling

Tutorials

Tutorials allow newcomers to quickly get their hands dirty with step-by-step instructions. It's therefore great that some of our community members have taken the time to demonstrate how they use molfeat.

Link	Name	Author	Description
	Practical cheminformatics	@PatWalters	This tutorial shows how to train a QSAR using just 8 lines of code, among others by utilizing tools from the datamol.io ecosystem.
	PyG GNN on Graphcore IPUs	@s-maddrellmander	This tutorial adapts the Training a GNN with PyG to show how to leverage Graphcore IPUs.
	Transformer on Graphcore IPUs	@s-maddrellmander	This tutorial adapts the Finetuning a pre-trained transformer to show how to leverage Graphcore IPUs.

Projects

Check out these awesome community projects that use Molfeat. | Link | Name | Author | Description | | ---------------------------------------------------------------------------------------------------------------------------------------------- | -------- | -------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | | AC Suite | @cmvcordova | The Activity Cliff (AC) Suite is a Pytorch Lightning + Hydra integrated utility to train and evaluate models for molecular property prediction based on the Matched Molecular Pair (MMP) abstraction. |

Code

From bug fixes to new features, code contributions directly benefit the molfeat package and everyone that uses it!