MMseqs2-GPU sets new standards in single query search speed, allows near instant search of big databases, scales to multiple GPUs and is fast beyond VRAM. It enables ColabFold MSA generation in seconds and sub-second Foldseek search against AFDB50. 1/n 📄 www.nature.com/articles/s41... 💿 mmseqs.com

Near-real-time protein structures change science: It means: → Next-gen protein AI data waves → Interactive protein design loops (DMTA in hours) → Proteome-scale insights with fewer resources It means the bottleneck doesn't have to be compute. It's close (preprint below).

Does anyone here care about biomolecular AI? Who should I follow?

🧬 Introducing La‑Proteina: a partially‑latent flow‑matching model that co‑generates sequence + all‑atom structure for proteins up to 800 aa 🧬 Side‑chains live in latents, backbone explicit → 75 % codesign & SOTA motif scaffolds 🔥

I'm at ICML 2025! DM me if you want to chat. @icmlconf.bsky.social #ICML2025 #icml25 #BioNeMo

Boltz-2 just dropped: open-source AI that predicts both protein complex folds ✚ binding affinities in one shot 🚀 This is a win for protein AI, but let's not forget MSAs, the bioinformatics backbone many structure models lean on.

MMseqs2-GPU is available as a downloadable NVIDIA NIM microservice (MSA-Search)!

📢📢 "Proteina: Scaling Flow-based Protein Structure Generative Models" #ICLR2025 (Oral Presentation) 🔥 Project page: research.nvidia.com/labs/genair/... 📜 Paper: arxiv.org/abs/2503.00710 🛠️ Code and weights: github.com/NVIDIA-Digit... 🧵Details in thread... (1/n)

I’m @neurips24! Let’s chat😁

"Are there at least 3 simulations per simulation condition with statistical analysis?" From @commsbio.bsky.social's "Reliability and reproducibility checklist for molecular dynamics simulations" (doi.org/10.1038/s420...) IMO the number 3 is meaningless and could equally well be 1 or 1000

Super excited to preprint our work on developing a Biomolecular Emulator (BioEmu): Scalable emulation of protein equilibrium ensembles with generative deep learning from @msftresearch.bsky.social ch AI for Science. www.biorxiv.org/content/10.1...

DiffDock was the first time a traditional drug discovery simulation task was represented as a generative AI task AFAIK. Recent DiffDock versions + other DL models are advancing rapidly + solving real problems for researchers. Let's have a balanced conversation about it. arxiv.org/abs/2412.02889

#CASP16 results are in! Template-based VFold seems to be lead method for nucleic acid structure prediction! AlphaFold2 and 3 still seem to be best methods for protein monomer and complex prediction.

Big day today - NVIDIA is open-sourcing the BioNeMo Framework, a toolkit of programming resources, libraries, and AI models designed for drug discovery. Built with GPU optimization in mind. nvidia.github.io/bionemo-fram...

If you're building AI models for drug discovery, you should check out the newly open-source #BioNeMo Framework: code: github.com/NVIDIA/bione... paper: arxiv.org/abs/2411.10548 docs: docs.nvidia.com/bionemo-fram... explainer: t.co/7MOamSChGN

Friends, Real-time, accurate protein structure prediction has never felt so imminent. Code: github.com/soedinglab/m... Publication: www.biorxiv.org/content/10.1... Blog: developer.nvidia.com/blog/boost-a... Press: blogs.nvidia.com/blog/japan-s...

developer.nvidia.com/blog/boost-a... Boost Alphafold2 Protein Structure Prediction with GPU-Accelerated MMseqs2 Nice improvements in speed

Now available – DiffDock 2.0 NIM This latest #NVIDIANIM update offers computational chemists and researchers a significant boost with 16% improved accuracy in identifying potential protein-small molecule interactions with Test for free: build.nvidia.com/mit/diffdock...

Now available – an updated RFdiffusion NIM This #NVIDIANIM enables researchers to efficiently design protein therapeutic candidates 1.9x faster due to accelerations in the inference engine, making their preclinical research smarter and less expensive. Test for free: build.nvidia.com/ipd/rfdiffus...

Accelerated computing is powering a new era in protein structure prediction, now with speed-of-light multiple sequence alignments. #MMseqs2GPU now makes #AlphaFold2 predictions faster and more efficient than ever. 1/🧵 developer.nvidia.com/blog/boost-a...

Accelerated computing is revolutionizing protein structure prediction, starting with lightning-fast multiple sequence alignments. Read more about #AlphaFold and #MMseqs2GPU: developer.nvidia.com/blog/boost-a...

Have you seen the innovation coming out of Japan recently? 👀 blogs.nvidia.com/blog/japan-s...

Analyzing extensive scientific literature and real-world evidence, Muse delivers comprehensive research and identifies optimal patient profiles and recruitment strategies, including materials tailored for diverse populations. formation.bio/blog/introdu...

I can't help but think TDC-2 is a peek into the future of therapeutic AI, where multimodal data integration and single-cell precision drive discovery. biorxiv.org/content/10.1...