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 add new featurizers to the molfeat ecosystem by extending its functionality with plug-and-play components. To learn more, see Extending molfeat.
|email@example.com||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 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.
|@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||This tutorial adapts the Training a GNN with PyG to show how to leverage Graphcore IPUs.|
|Transformer on Graphcore IPUs||This tutorial adapts the Finetuning a pre-trained transformer to show how to leverage Graphcore IPUs.|
From bug fixes to new features, code contributions directly benefit the molfeat package and everyone that uses it!