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The rebirth of #MLCB2019 was great. Great posters and talks all around. Thanks to the organizers and everyone who came up for making it awesome. Let's make this happen every year---but with coffee during the coffee break in the future.

We have 2 papers published in @nature today! 🎉 One describes AlphaFold, which uses deep neural networks to predict protein structures with high accuracy. AlphaFold made the most accurate predictions at the 2018 scientific community assessment CASP13. 1/4 deepmind.com/blog/article/A…

Somehow this slipped my radar. Very cool looking work from the Dror Lab: Hierarchical, rotation-equivariant neural networks to predict the structure of protein complexes. arxiv.org/abs/2006.09275

Excited to present our latest work on geometric prediction: the class of prediction problems for (non-scalar) geometric tensors! We show the first real-world demonstration of geometric prediction without the need for scalar approximations. arxiv.org/abs/2006.14163 [1/n]

Machine Learning in Structural Biology (MLSB @ NeurIPS) is accepted at #NeurIPS2020! Come check out the exciting line-up of speakers and dates for the call for papers at mlsb.io (more details coming soon). Register interest at forms.gle/zLyQupP93TDqpA…

Check out our latest paper exploring the effects of GPCR phosphorylation on arrestin signaling with great collaborators @MattMasureel, Kobilka lab, and Michel Bouvier!

We are excited to present our work on Geometric Vector Perceptrons, an equivariant GNN architecture for residue-level protein graphs, at ICLR! Check out our spotlight talk iclr.cc/virtual/2021/s… and Github repo github.com/drorlab/gvp-py….

AlphaFold for RNA? Excited to share our Stanford AI Lab work on deep learning for predicting 3D RNA structure, out today on the cover of Science Magazine! science.sciencemag.org/content/373/65… Sound interesting? Join us at atomic.ai to bring this work to life! More news soon!

Now out on the cover of Science Magazine: Our work with Das Lab on predicting 3D RNA structures! science.sciencemag.org/content/373/65… Check out the official press release: news.stanford.edu/2021/08/26/ai-… and our earlier work on protein complexes: doi.org/10.1002/prot.2…

A new paper from our lab, Monash Institute of Pharmaceutical Sciences, and @KarunaPharma uses atomic-level simulations to reveal a molecular mechanism by which a ligand can achieve selectivity between nearly identical receptors. Open access: nature.com/articles/s4158…

How accurately can one predict drug binding modes using AlphaFold models? New work from our lab reveals AF2's improved accuracy in capturing binding pocket structures, but the results of docking are not a slam dunk. 💥🧐 Check out the preprint: biorxiv.org/content/10.110…