Post #842

GraphML News (May 25th) - Aurora, primer on MD, PoET for proteins The main NeurIPS deadline has finally passed - congrats to those who made it to the submission, you deserved some decompression time! (and reviewers behold, 20k submissions are coming). We could probably expect a flurry of preprint on arxiv next weeks - we’ll keep you posted about the most interesting things. 🌍 MSR AI 4 Science presented Aurora - a foundation model of the atmosphere that works for weather forecasting, air pollution, and predicting rare weather events. Aurora improves over the recent GraphCast and does so with plain vanilla Perceivers and ViTs, no equivariance involved 🥲 ⚛️ Abishaike Mahajan prepared a great primer on molecular dynamics for complete beginners gradually introducing most important concepts (with illustrations) from force fields to equilibration to computational simulation methods. Finally, the article touched upon some successful use-cases of MD in industry. Highly recommended read to grasp the basics. ✍️ Meanwhile, folks returning from ICLR share some reflections on their fields - for instance, Patrick Schwab (GSK) on the papers for ML for Drug Discovery, and Lindsay Edwards (Relation) on why AI for DD is difficult. 🧬 Openprotein released PoET (the protein evolution transformer) - a protein LM that significantly outperforms ESM-2 in zero-shot prediction on ProteinGym while being much smaller. The authors project that a 200M PoET model can be equivalent to a 500B ESM model (by extrapolating scaling laws a bit). The checkpoint and inference code are publicly available. Weekend reading: Deep Learning for Protein-Ligand Docking: Are We There Yet? by Alex Morehead et al. - introduces the PoseBench benchmark for docking and evaluated a handful of modern baselines (DiffDock-L leads in most cases) Explaining Graph Neural Networks via Structure-aware Interaction Index (ICML’24) by Ngoc Bui et al. feat Rex Ying: Myerson-Taylor instead of Shapley methods Fisher Flow Matching for Generative Modeling over Discrete Data by Oscar Davis feat. Michael Bronstein and Joey Bose - flow matching for discrete data, already outperforms a recent discrete FM model DirichletFM