Alex Morehead (何聪) @MoreheadAlex
Hopper Postdoctoral Fellow @BerkeleyLab. Prev: #MachineLearning & #CompBio PhD @Mizzou; Research Intern @ Profluent & Absci. #DeepLearning & #GenerativeModels. amorehead.github.io Berkeley, California, USA Joined December 2012-
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📢 Only two days left to submit to LoG 2026! We welcome submissions on all areas broadly related to learning on graphs and geometry. 🔗 logconference.org/cfp/ 📅 Key dates (AoE): • July 29 – Abstract deadline • August 1 – Full paper deadline
🧬 MegaFold: Efficient AlphaFold-3 Training Across NVIDIA and AMD GPUs MegaFold will be presented at ISC High Performance 2026! 🗺️Research Paper Session: ML Systems and Frameworks 🗓️ Wednesday, June 24 · 1:00–2:00 PM (CEST) 📍Hall E, 2nd Floor, Hamburg, Germany Come find out how we train AlphaFold-3 on 3.36× longer sequences, up to 1.73× faster, on both NVIDIA and AMD GPUs. Using MegaFold, we can successfully train on H200s, MI250 and MI325X. 🚀 Think training AlphaFold-3 (AF3) is just like training an LLM? Think again. Despite having only ~500M parameters, AF3 is up to two orders of magnitude slower to train than a size-equivalent LLM and OOMs on sequences as short as 590 residues. Why? One operator: 3D attention over 2D pairwise representations. It makes compute and memory scale cubically with sequence length, blowing up activation memory, fragmenting execution into tens of thousans of tiny kernels per iteration, and starving the GPU while the CPU grinds through evolutionary-context preprocessing. In our latest work, MegaFold, we diagnose exactly why AF3 training is slow and build an end-to-end system that fixes it across the entire stack. The results: ✅ 3.36× longer trainable sequences on 32 GPUs ✅ Up to 1.73× faster per-iteration training (1.62× on AMD) ✅ 23% lower peak memory ✅ Runs on both NVIDIA H200, AMD MI250 & AMD MI325x, fully cross-platform 🔧 Four key innovations: - EvoFlash-3D: a cross-platform Triton kernel that computes 3D attention without ever materializing the cubic attention map (cutting EvoAttention memory by 4.24×). - EvoSP-3D: a communication-efficient sharding strategy built for alternating-axis 2D pairwise representations - EvoFusion: a fused operator stack that tames AF3's kernel fragmentation - EvoPipe: a determinism-aware host-device pipeline that eliminates preprocessing stalls And it's verified: MegaFold matches baseline training loss step-for-step, so you get all the speed and memory savings with zero impact on convergence. Huge thanks to our students Hoa La and Ahan Gupta for working hard on this project, and to our collaborators Jianlin Cheng and Alex Morehead for their valuable contributions, feedback, and support throughout the work! MegaFold is fully open source. 🎉 💻 Code: github.com/Supercomputing… 🔗 Project page: supercomputing-system-ai-lab.github.io/projects/megaf… 📄 Paper: arxiv.org/pdf/2506.20686 📝 Blog: supercomputing-system-ai-lab.github.io/blogs/blog/meg… See you at ISC, Hall E! 👋
@cohenrap @lazar_atan @frimpongboadu1 @joelselvaraj95 @AlexanderTong7 @ask1729 @jianlincheng Apologies, just got access to a (free) shareable link: rdcu.be/ffgfF
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/BioinfoMachine…
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/a… Code: github.com/amorehead/awes…
🔬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.github.io/projects/megaf… 📄 Paper: arxiv.org/pdf/2506.20686 💻 Code: github.com/Supercomputing… 🤝 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/BioinfoMachine…
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/BioinfoMachine…
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/BioinfoMachine…
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/BioinfoMachine…
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.
Introducing PoseBench, the first deep learning (DL) benchmark for practical protein-ligand docking, which provides actionable insights for the development of future docking methods. 🧵 Paper: arxiv.org/abs/2405.14108 Code: github.com/BioinfoMachine…
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 👀👇🏻
🧬🤖 Introducing RNA-FrameFlow –– an unconditional generative model for 3D RNA backbone design! 📑: arxiv.org/abs/2406.13839 🧰: github.com/rish-16/rna-ba… Our method generates ≥ 40% self-consistent *all-atom* RNA backbones that are globally and locally realistic 💪🏻 1/9
Examples of pocket-specific 3D molecules generated by GCDM:
I'm excited to announce that GCDM for 3D molecule diffusion generation and optimization is now published in Nature @CommsChem! Paper: nature.com/articles/s4200… Code: github.com/BioinfoMachine…
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.
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