Post #848

GraphML News (June 22nd) - $30M seed for CuspAI, Graph Foundation Models, MoML 2024 💸 A new startup CuspAI by Max Welling and Chad Edwards focusing on materials discovery and materials design for clean energy and sustainability raised $30M in the seed round (led by Hoxton, Basis Set, and Lightspeed). The support from the godfathers is significant - Geoff Hinton is a board advisor and Yann LeCun commented on the collaboration with FAIR and OpenCatalyst teams on OpenDAC. The materials design area gets hotter - not as hot as drug discovery and protein design though - but is steadily growing. In addition to Radical AI, Orbital Materials, new CuspAI, a fresh Entalpic by ex-Mila founders raised $5M+. 🔖 Together with Michael Bronstein, we released a new blog post on Graph Foundation Models. First, we define what GFMs are and what are the key design challenges covering heterogeneous model expressivity, scaling laws, and data scarcity. Then, we describe several successful examples of recent generalist models that can be considered GFMs in a particular area, eg, GraphAny for node classification, ULTRA for KG reasoning, and MACE MP-0 as universal potentials. We made sure to include all the recent references including position papers to appear at ICML’24! 🧬 The Molecular ML 2024 conference took place in Montreal this week (concluding the ML for Drug Discovery summer school) and featured talks on drug discovery and drug design. The recording is already available - check out talks by Jian Tang (BioGeometry) on geometric DL for proteins and by Max Jaderberg (Chief AI Officer at Isomorphic Labs) on AlphaFold 3. Might be one of the first public talks on AF3! Weekend reading: More benchmarks (brought to you by the NeurIPS Datasets & Benchmarking track deadline). Temporal Graph Benchmark 2.0 by Gastinger, Huang et al - the first large-scale benchmark for temporal KGs and heterogeneous graphs Text-space Graph Foundation Models by Chen et al feat. Anton Tsitsulin and Bryan Perozzi - a collection of text-attributed graphs for node classification, link prediction, and graph-level tasks Towards Neural Scaling Laws for Foundation Models on Temporal Graphs by Shirzadkhani, Ngo, Shamsi et al - perhaps the first evidence that one temporal GNN can generalize to different temporal graphs (here those are token transactions in Ethereum) RNA-FrameFlow: Flow Matching for de novo 3D RNA Backbone Design by Rishabh Anand, our own Chaitanya K. Joshi, et al - equivariant flow matching for generating 3D RNA structures.