Post #770

Graph ML News (May 13th) - $100M Wall Street Edition 💸 Perhaps the biggest news of the month: Recursion, a major player in drug discovery, acquires two startups: Valence Discovery (Mila, Montreal) for $47.5M and Cyclica (Toronto) for $40M. Not pretending to wear a Wall Street market analyst hat, I’d speculate those are the biggest M&A deals of the past years in the Graph ML industry. Graph ML and Geometric Deep Learning are at the core of modern drug discovery powering pretty much all stages of the pipeline reducing the time to market from standard 10 years by a factor of 2-3x. I happen to know many smart folks from Valence Discovery including Prudencio Tossou, Therence Bois, and Dominique Beaini with whom we co-authored a few papers for NeurIPS’22. Valence also supports the most popular public reading groups on Graph ML: Learning on Graphs and Geometry (LOG2) and Molecular Modeling & Drug Discovery (M2D2) covering hot new papers with original authors. Big congratulations to the team and hope we’ll see more cool stuff in the future! With the Wall Street hat on, I’d hypothesize the next big wave of investment rounds and huge M&As would be in the material discovery and AI4Science fields where Geometric DL is at the core either. Venues: ECML PKDD’23 in Torino published the list of accepted workshops - have a look at the Mining and Learning with Graphs (MLG) workshop featuring keynotes from Bastian Rieck and Giannis Nikolentzos. Bastian gives amazing talks on topology, highly recommend to attend if you are at ECML this year. Paper submission deadline is June 12th, consider submitting as well. Weekend reading: Alex Barghi wrote a blogpost introducing the new cuGraph backend of PyG covering new accelerated primitives, feature store, and neighbor sampling using node classification on the MAG graph as example. Zhaocheng Zhu posted a viral tweet with the Colab Notebook comparing PyTorch and JAX performance of common GNN operators. Key takeaways are: JAX with JIT is faster than PyTorch on homogeneous graphs, and much faster and memory-efficient on larger heterogeneous graphs when PyTorch throws OOM; new torch.compile() often makes the code 2x faster than vanilla torch, so make sure to update your envs to torch 2.0 🚀 New papers for the weekend reading: Language models can generate molecules, materials, and protein binding sites directly in three dimensions as XYZ, CIF, and PDB files by Daniel Flam-Shepherd and Alan Aspuru-Guzik - “In this work, we show how language models, without any architecture modifications, trained using next-token prediction - can generate novel and valid structures in three dimensions from various substantially different distributions of chemical structures.” Sparks of chemical intelligence 👀 Advancing structural biology through breakthroughs in AI by Laksh Aithani and folks from Charm Therapeutics - a nice introductory survey how (Geometric) DL transforms structural biology.