@DrorLab

The Dror Lab at Stanford. For all your computational needs: MD, ML, HPC... Applied to biochemistry, cell biology, drug discovery, and more!

 Stanford, California
drorlab.stanford.edu
Joined October 2019
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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/10.1101/…

How accurately can one predict drug binding modes using AlphaFold models?

Computational prediction of protein structure has been pursued intensely for decades, motivated largely by the goal of using structural models for drug discovery. Recently developed machine learning...

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A new paper from our lab, @MIPS_Australia, 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/s41589-0…

Structural basis of efficacy-driven ligand selectivity at GPCRs

Nature Chemical Biology - The use of atomic-level simulations reveals a molecular mechanism by which a ligand can achieve selectivity between nearly identical receptors, enabling the rational...

nature.com
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Now out on the cover of @ScienceMagazine: Our work with @RDasLab on predicting 3D RNA structures! science.sciencemag.org/conte… Check out the official press release: news.stanford.edu/2021/08/26… and our earlier work on protein complexes: doi.org/10.1002/prot.26033
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AlphaFold for RNA? Excited to share our @StanfordAILab work on deep learning for predicting 3D RNA structure, out today on the cover of @ScienceMagazine! science.sciencemag.org/conte… Sound interesting? Join us at atomic.ai to bring this work to life! More news soon!
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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/spotlig… and Github repo github.com/drorlab/gvp-pytor….
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Check out our latest paper exploring the effects of GPCR phosphorylation on arrestin signaling with great collaborators @MattMasureel, Kobilka lab, and @Michel_Bouvier!
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Looking for new challenges for machine learning in structural biology? Check out our recent release: ATOM3D, a unified collection of diverse benchmark datasets for biological problems that deal with atom coordinates in 3D space. ⚛️⚛️⚛️ atom3d.ai/ [1/4]

ATOM3D: Tasks on Molecules in Three Dimensions

A collection of benchmark datasets for machine learning in structural biology

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The corresponding code to load, filter, and split the ATOM3D datasets is maintained on @github: github.com/drorlab/atom3d. We hope this lowers the entry barrier for algorithm developers and promotes 3D atomic data as a “machine learning datatype” in its own right. [3/4]

GitHub - drorlab/atom3d: ATOM3D: tasks on molecules in three dimensions

ATOM3D: tasks on molecules in three dimensions. Contribute to drorlab/atom3d development by creating an account on GitHub.

github.com
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We benchmark 3 prototypical architectures - 3D conv. networks, graph networks and equivariant networks - and compare them to 1D/2D baselines. We find that 3D info can strongly improve model performance, but it depends on the choice of architecture for a particular task. [4/4]
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Machine Learning in Structural Biology (@workshopmlsb) 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/zLyQupP93TDqpAf39
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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]
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Somehow this slipped my radar. Very cool looking work from the @DrorLab: Hierarchical, rotation-equivariant neural networks to predict the structure of protein complexes. arxiv.org/abs/2006.09275
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Excited to share our work on learning from the 3D structure of macromolecules! Our neural network architecture enables us to learn directly from all atoms in protein complexes containing tens of thousands of atoms: arxiv.org/abs/2006.09275 (1/3)
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Starting with just the element type of each atom, we learn features at different levels of structural coarseness and aggregate this information hierarchically. The rotation-equivariant network recognizes molecular motifs independent of their orientation. (2/3)
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Such a method is readily applicable to other tasks involving learning on 3D structures of large atomic systems. (3/3)
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Excited to present two papers just out in Science on how to create more effective drugs with fewer side effects! science.sciencemag.org/conte…
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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/Al…

AlphaFold: Using AI for scientific discovery

In our study published in Nature, we demonstrate how artificial intelligence research can drive and accelerate new scientific discoveries. We’ve built a dedicated, interdisciplinary team in hopes of...

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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.
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