Post #814

GraphML News (Dec 23rd) - Antibiotics discovered with GNNs, OpenCatalyst 23, TF GNN A group of MIT and Harvard researchers reported (in the recent Nature paper) the discovery of a new class of antibiotics. The screening process was supported by ChemProp, a suite of GNNs for molecular property prediction. The authors trained an ensemble of 10 models to filter down the initial space of 11M compounds to 1.5K compounds. Most of those models are 5-layer MPNNs with hidden size of 1600. Pre-trained checkpoints and notebooks are available in the GitHub repo of the project. Exciting times for the field (and many bio startups)! 👏 The Open Catalyst project announced the winners of the recent OCP 23 challenge (aka AdsorbML) - the top approaches build around Equiformer V2 with the best model reaching 46% success rate. It is likely that the numbers can be bumped even further by training on even larger OCP splits as demonstrated by eSCN Large and Equiformer V2 in the paper. Google released TensorFlow GNN v1.0, the library you can run in production on GPUs and TPUs. Heterogeneous graphs are of particular focus - have a look at the example notebooks to learn more. We’ll probably take a break with the news the next week to enjoy the holiday season and get back in January with the massive year-review post. 🥂 Weekend reading: Perspectives on the State and Future of Deep Learning - 2023 - opinions of prominent ML researchers (incl. Max Welling, Kyunghyun Cho, Andrew Gordon Wilson, and ChatGPT, lolz) on the current problems and challenges. High-quality holiday reading 👌 Graph Transformers for Large Graphs by Vijay Prakash Dwivedi feat. Xavier Bresson, Neil Shah. Scaling GTs to graphs of 100M nodes. Harmonics of Learning: Universal Fourier Features Emerge in Invariant Networks by Giovanni Luca Marchetti et al. Turns out Fourier features do emerge in neural networks and help to identify symmetries. The nature of the Fourier kernels looks quite similar to the steerable kernels for irreducible representations