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Post #877

@graphml

Graph Machine Learning

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Publié8 déc.08/12/2024 05:55
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GraphML News (Dec 8th) - NeurIPS’24, ESM Cambrian, Antiviral Competition, The Well 🍻 NeurIPS 2024 starts next week, the full schedule is available, the main conference is scheduled for Wed-Fri with two days of workshops (Sat-Sun) and unknown amount of private parties and gatherings throughout the week. See you in Vancouver! 🧬 EvolutionaryScale announced ESM Cambrian (ESM C), a new family of embedding models replacing ESM-2 with better performance across all sizes (300M, 600M, and 6B), dramatically smaller memory requirements and faster inference (think of Triton kernels here). ESM C was trained on UniRef, MGnify, and JGI data, smaller models are already available on GitHub, the 6B is available through the API service. 💊 Polaris Hub launches the Antiviral Competition together with ASAP discovery and OpenADMET. The competition includes three tracks: - Predicting ligand poses of MERS-CoV based on SARS-CoV2 structures (metric: RMSD) - Predicting ligand fluorescence potencies based on SARS and MERS data (metrics: MAE of pIC50 and ranking) - Predicting ligands’ ADMET properties (MAE and ranking) The competition starts on Jan 13th and ends on March 25th, prepare your big GNNs ⚔️ ⚛️ After the announcement in May, MSR released the code and weights of MatterSim, a universal ML potential akin to MACE-MP-0 and Orb models. MatterSim is based on the M3GNet message passing GNN and is available in 1M and 5M params versions. 🪣 Polymathic AI, Flatiron Institute, and a collab of universities and national labs released The Well, a 15 TB dataset of physical simulations (think PDEs and Neural Operators) covering 16 different areas from fluid dynamics to supernova explosions. In the accompanying preprint, the authors compared several variants of Fourier Neural Operators (FNO) and U-Nets. A great resource for scientific and industrial applications where expensive simulations eat up a huge bulk of supercomputers time.