Post #875

GraphML News (Nov 23rd) - LOG 2024, Boltz-1 and Chai-1r, OCx24, cuEquivariance 🎙️LOG 2024 starts next Tuesday! The schedule of orals and posters is already available, registration is free, we’ll be happy to see you there online or at one of many local meetups! 🧬 The “Stable Diffusion moment” in generative models for structural biology is spinning up: two models released during the past week starting with fully open-source Boltz-1 from MIT (tech report, code) that achieves AF3-like quality and outperforms a recent Chai-1 (open inference code). Meanwhile, Chai folks released Chai-1r that supports complexes with user-specified restraints - and already compared with Boltz-1. Open source and competition really drive the field forward 👏 The next step for AF3-like models seems to be integrating the recent GPU-enabled MSA tool MMSeqs2-GPU that might shave off another order of magnitude of inference time of protein structure prediction models. 🧊 FAIR Chemistry at Meta, University of Toronto, and VSParticle presented Open Catalyst experiments (OCx24) - a new dataset of 600 mixed metal catalysts synthesized and probed physically in the lab (a huge step beyond DFT-only simulations) along with analytical data for 19k catalyst materials with 685M relaxations. It’s already the 3rd huge dataset openly published by Meta in addition to OpenDAC and OMAT - Meta is a firm leader in this area of AI 4 Science. Fun fact: models from the Open Catalyst ecosystem are directly used in Meta’s products like recent Orion AR glasses. 🔱 Faster spherical harmonics and tensor products for geometric GNNs: following EquiTriton, an open-source collection of Triton kernels for fast and memory-efficient computation of spherical harmonics developed by Intel Labs (enabling harmonics of up to the 10th order), NVIDIA released cuEquivariance - CUDA kernels (closed-source kernels with public bindings for PyTorch, JAX, and numpy) for spherical harmonics and tensor products which speed up DiffDock, MACE and other models by 2-20x, this is especially useful in tasks where a model is called multiple times like a generative model or MD calculations. cuEquivariance is a part of the new BioNeMo suite for drug discovery. Weekend reading: 📚 Check out accepted orals and posters of LOG 2024 on OpenReview