Graph ML in 2023: The State of Affairs

Generative diffusion models in the vision-language domain were the headline topic in the Deep Learning world in 2022. While generating images and videos is definitely a cool playground to try out different models and sampling techniques, we'd argue that In our recent article, we were pondering whether "Denoising Diffusion Is All You Need?". There, we reviewed newest generative models for graph generation (DiGress), molecular conformer generation (EDM, GeoDiff, Torsional Diffusion), molecular docking (DiffDock), molecular linking (DiffLinker), and ligand generation (DiffSBDD). Chroma from Generate Biomedicines 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; RoseTTaFold Diffusion (RF Diffusion) from the Baker Lab and MIT is packed with the similar functionality also allowing for text prompts like "Generate a protein that binds to X" as well as being capable of functional motif scaffolding, scaffolding enzyme active sites, and de novo protein design. Strong point: 1000 designs generated with RF Diffusion were experimentally synthesized and tested in the lab!