Introducing Zatom-1, the first end-to-end, fully open-source foundation model for 3D chemistry! This was a great collaborative effort with many brilliant scientists. I'm grateful to have played a small part. Paper: arxiv.org/abs/2602.22251 Code: github.com/Zatom-AI/zatom

A review of flow matching and its role in CompBio/virtual cell research, now in Nature Machine Intelligence! Congrats, team! (@lazar_atan, Akshata Hegde, Yanli Wang, @frimpongboadu1, @joelselvaraj95, @AlexanderTong7, @ask1729, @jianlincheng) Paper: tinyurl.com/3jj4k6c3

Excited to announce that PoseBench is now published in Nature Machine Intelligence! Paper: rdcu.be/eW5oj Code: github.com/BioinfoMachineLea…

Interested in learning more about how flow matching has begun to advance bioinformatics and computational biology? And how it has already started making strides towards the development of an AI-based virtual cell? Paper: authorea.com/users/637193/ar… Code: github.com/amorehead/awesome…

🔬Interested in training AlphaFold3 faster, at scale, and beyond NVIDIA GPU? Now you can. AlphaFold3 is a major leap in biomolecular modeling, but behind the scenes, it introduces severe system bottlenecks: 🧠 2D EvoAttention spikes memory usage 📉 Retrieval-augmented training pipeline causes long GPU idle time ⛔ Frequent but memory-intensive ops slow everything down Today, I'm excited to announce MegaFold, a fully open-source system to make AlphaFold3 training fast, scalable, and cross-platform on both NVIDIA and AMD GPUs. MegaFold delivers: ⚡ Up to 1.73x / 1.62x faster training on NVIDIA H100 / AMD MI250 🧬 Up to 1.35× longer sequences compared to PyTorch baseline Key features: 🚀 Memory-Efficient EvoAttention via portable Triton kernels 💡 Ahead-of-Time Caching to eliminate GPU idle time in retrieval pipelines 🔗 DeepFusion for reducing overhead of small but frequent memory-intensive AF3 ops 📘 Project page: supercomputing-system-ai-lab… 📄 Paper: arxiv.org/pdf/2506.20686 💻 Code: github.com/Supercomputing-Sy… 🤝 MegaFold is developed in collaboration between UIUC SSAIL Lab and researchers from University of Missouri and Lawrence Berkeley National Laboratory. Kudos to the brilliant team: Hoa La, Ahan Gupta, Alex Morehead, Jianlin Cheng #AlphaFold3 #AI #ProteinFolding #Bioinformatics #AMD #Triton #CrossPlatform #OpenSource

Delighted to announce that FlowDock has been accepted to ISMB 2025. See you all in Liverpool! Paper: arxiv.org/abs/2412.10966 Code: github.com/BioinfoMachineLea…

v0.6.0 of PoseBench is now available, featuring (1) results for AlphaFold 3, the new PLIF-WM metric, and (3) the new DockGen-E dataset of challenging docking targets. See the GitHub release below for more details. Paper: arxiv.org/abs/2405.14108 Code: github.com/BioinfoMachineLea…

Excited to release FlowDock, an all-atom flow matching model for generative protein-ligand docking and affinity prediction (ranked as a top method in CASP16)! Paper: arxiv.org/abs/2412.10966 Code: github.com/BioinfoMachineLea…

PoseBench v0.5.0 is now released, featuring (1) docking results with AlphaFold 3's predicted protein structures, (2) Chai-1's benchmarking results, and (3) support for running exhaustive HPC benchmarking sweeps. 🧪 Paper: arxiv.org/abs/2405.14108 Code: github.com/BioinfoMachineLea…

At @icmlconf this week where I'm presenting PoseBench at the AI4Science workshop as a spotlight. I'll also give an oral presentation on RNA-FrameFlow at the SPIGM workshop (AI4Science spotlight as well!) on behalf of many amazing collaborators including @rishabh16_ and @chaitjo.

Unfortunately can't join in-person @icmlconf 🇦🇹 but our awesome co-author @MoreheadAlex will be there!!! Check out our Oral presentation @ SPIGM Workshop on 26 July and Spotlight poster @AI_for_Science Workshop on 27 July ✨🥳 See our poster schedule below 👀👇🏻

GCDM also generates significantly more PoseBusters-valid 3D molecules in target protein pockets.

GCDM enables out-of-the-box (reliable) property and stability-specific optimization of existing 3D molecules.

GCDM generates more than twice as many PoseBusters-valid large (i.e., GEOM-Drugs-sized) 3D molecules compared to existing methods.

GCDM generates 3D molecules with specific molecular properties more accurately and stably compared to existing methods.

New benchmark results for the DockGen dataset from the recent DiffDock-L paper:

LoG Conference 2024 is back !!!👉 We are looking for more reviewers! We have a special emphasis on review quality via monetary rewards, a more focused conference topic, and low reviewer load (max 3 papers). But for this we need your help! Sign up here: forms.gle/Nuff4ndVZDFTisb38!