Post #820

GraphML News (Jan 20th) - More Blogs, MACE pre-trained potentials, AlphaFold 🤝 Psychedelics ICLR 2024 announced the accepted papers together with orals and spotlights — we’ll probably make a rundown on the coolest papers but meanwhile you can check one-line tl;dr’s by the famous Compressor by Vitaly Kurin. See you in Vienna in May! 📝 In addition to the megapost on the state of affairs in Graph & Geometric ML, the community delivered two more reviews: - On Temporal Graph Learning by Shenyang Huang, Emanuele Rossi, Michael Galkin, Andrea Cini, Ingo Scholtes. - On AI 4 Science by the organizers of the AI for Science workshops (that you see at all major ML venues) including Sherry Lixue Cheng, Yuanqi Du, Chenru Duan, Ada Fang, Tianfan Fu, Wenhao Gao, Kexin Huang, Ziming Liu, Di Luo, and Lijing Wang ⚛️ The MACE team released two foundational ML potential checkpoints: MP for inorganic crystals from the Materials Project and OFF for organic materials and molecular liquids. We covered those in the previous posts — now you can run some MD simulations with them on a laptop. 🍭 AlphaFold discovers potentially new psychedelic molecules (thousands of candidates!) - practically, those can be new antidepressants (would some researchers be willing to try some just for the sake of science and scientific method?) Besides, the article mentions some works that apply AlphaFold to target G-protein-coupled receptors (GPCR). Apart from having its own Wiki page, GPCR was the main subject of the 2012 Nobel Prize in chemistry. The Nobel Prize for AlphaFold seems even closer? Weekend reading: You want to say you finished all those blogposts? 😉