1/ 🚀 Introducing AIDO.StructureDiffusion: A generative model for structural protein design—enabling high-quality, controllable generation of monomers, complexes, and antibodies. 🧵

Have you signed up yet? GenBio AI Co-Founder and Chief Scientist @ericxing will deliver a keynote at #PyTorchConf in San Francisco on October 23 at 2:46 PM PT. He will share GenBio AI’s latest progress on world models, deep reasoning agents, and virtual cell models, alongside

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🚀 Introducing AIDO.DNA2, GenBio AI’s next-generation multi-species genomic foundation model. Built with a Mixture-of-Experts architecture and trained on the massive OpenGenome2 dataset, AIDO.DNA2 delivers higher accuracy, better efficiency, and stronger generalization across

📢 Happening today! GenBio AI Co-Founder & CTO @dasongle will be presenting at the MedInvest Biotech & Pharma Investor Conference in Palo Alto, CA. 📍 Session: Decode Biology Holistically, Revolutionize Biomedicine 🗓️ September 24, 2025 – 3:20 PM PT We are proud to

5/ 📝 Read the blog summary:

4/ 📄 Read the full paper on bioRxiv:

3/ Applications include: • CRISPR screens • Drug response modeling • Designing experiments for rare populations • Cross-study integration across labs and protocols

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2/ Why it matters: Most models blur cell state with perturbation effect. This makes it hard to generalize and hides failures in rare cell types. State-conditioned modeling separates the two, making results more interpretable, transferable across datasets, and robust to batch

1/ 🔬 New talk at the Foundation Models for Biology Seminar Series (FM4Bio) by GenBio AI: @abhinadduri introduces state-conditioned perturbation modeling, a framework for predicting how edits, drugs, or doses affect cells depending on their state. 📺 Watch:

⏰ Happening tomorrow! Don’t miss the next Foundation Models for Biology (#FM4Bio) Seminar on September 17 at 9 AM PT. @HAOTIANCUI1 will present “Large Models for Single-Cell Omics and Drug Discovery: Data, Pretraining, and Closed-Loop Environment.” The talk will cover the

📡 Join us for the next #FM4Bio Seminar on Sept 17 at 9 AM PT featuring @HAOTIANCUI1 Haotian will present “Large Models for Single-Cell Omics and Drug Discovery: Data, Pretraining, and Closed-Loop Environment.” Save your spot → https://t.co/1nbSMlvkGU

GenBio AI Co-Founder & Chief Scientist @ericxing will deliver a keynote presentation at the #PyTorchConf in San Francisco on Oct 23 at 2:35 PM PT. He will join global leaders in AI, ML, and Open-Source to share insights on the future of machine learning. Talk details TBA! 📌

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3/ Why it matters: • Accelerates drug discovery • Improves bio-organic & biomolecular modeling • Makes quantum-informed simulation practical for everyday use 📝 Read our blog summary:

2/ ⚖️ The challenges in molecular simulation: • DFT = gold-standard accuracy, but painfully slow • Force fields = fast, but often imprecise Egret-1 bridges this gap with DFT-like accuracy at a much lower cost, trained on high-level quantum data. 📄 Read the paper:

1/ 🚀 New from the Foundation Models for Biology Seminar Series (#FM4Bio) at GenBio AI: 🔬 @manntis4 introduces Egret-1, a family of large pretrained neural network potentials (NNPs) for efficient & accurate bio-organic simulation. 🧵 👇 https://t.co/OlcAgf7tbd

Thanks @ChanZuckerberg for inviting me to the @westendlabs last week for the Cell Biology at Scale (CB@S) meeting especially the modeling talks (picture shows Eric Xing of @genbioai )

Happening now! →

⏰ Happening tomorrow! Don’t miss the next Foundation Models for Biology (#FM4Bio) Seminar on Aug 5 at 9 AM PT. @abhinadduri will present “Predicting Cellular Responses to Perturbation Across Diverse Contexts With STATE.” STATE is a machine learning architecture for predicting

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4/ This work strengthens AIDO’s vision: multiscale foundation models for predicting, simulating, and programming biology across contexts, from molecules to phenotypes. Read more ↓

3/ Why this matters for AIDO: • Generating personalized models on-demand for every individual • Unified ML paradigm for multi-modal data integration • Information flow from molecular to cellular scale improves disease models Paper 🔗