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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 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
github molfeat-padel Adds support for the PaDeL descriptors, as introduced by Yap, 2010. This is the official exemplary plugin for molfeat.
github 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.

Link Name Author Description
Open In Colab Practical cheminformatics @PatWalters This tutorial shows how to train a QSAR using just 8 lines of code, among others by utilizing tools from the ecosystem.
Run on Gradient PyG GNN on Graphcore IPUs @s-maddrellmander This tutorial adapts the Training a GNN with PyG to show how to leverage Graphcore IPUs.
Run on Gradient Transformer on Graphcore IPUs @s-maddrellmander 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!