Friday News: PyG 2.2 and Protein Diffusion Models

For those who are at the NeurIPS workboat 2022, Saturday and Sunday are days of workshops on graph learning, structural biology, physics, and material discovery. Apart from that,

The PyG team has finally released PyG 2.2.0, the first version to feature the super-optimized pyg-lib that speeds up GNNs and sampling on both CPUs and GPUs (sometimes up to 20x!). The 2.2 update also includes new FeatureStore and GraphStore with which you can set up communications with large databases and graphs too big to store in memory. Time to update your envs ⏰

Generate Biomedicines releases Chroma, an equivariant conditional diffusion model for generating proteins. The conditional part is particularly cool as we usually want to generate proteins with certain properties and functions - Chroma allows to impose functional and geometric constraints, and even use natural language queries like “Generate a protein with CHAD domain” thanks to a small GPT-Neo trained on protein captioning. The 80-pager paper is on the website, and you can have a look at the thread by Andrew Beam.

Simultaneously, the Baker Lab releases RoseTTa Fold Diffusion (RF Diffusion) packed with the similar functionality also allowing for text prompts like “Generate a protein that binds to X”. Check out the Twitter thread by Joseph Watson, the 1st author. The 70-pager preprint is available, so here is your casual weekend reading of two papers 🙂