Proteo-R1 (ICML 2026), the first reasoning protein foundation model for protein design, is out! 🚀🧬 Most protein design models generate structures without ever *reasoning* about which residues matter. We think that's backwards. Human protein engineers👩‍🔧 don't work this way. They identify critical interaction residues first — charged anchors, hydrophobic hotspots, specificity-determining motifs — and only then optimize geometry around those decisions. ━━━━━━━━━━━━━━━━ 🔬 THE CORE IDEA ━━━━━━━━━━━━━━━━ A dual-expert architecture that explicitly decouples molecular understanding from geometric generation: → ⚡A multimodal LLM (understanding expert) analyzes protein sequences, structures, and text to identify key functional residues governing binding and specificity → ⚡A diffusion model (generation expert) then co-designs sequence + structure — but with those residues locked in as hard constraints ━━━━━━━━━━━━━━━━ 📐 HOW IT'S TRAINED ━━━━━━━━━━━━━━━━ Three-stage curriculum: ① Multimodal Alignment — freeze the LLM, train projections to bridge ESM-2 + AF3-style structural features into language space ② Structural Reasoning Mid-Training — unfreeze the LLM, teach it residue grounding → pairwise geometry → interface localization → hotspot prediction ③ Joint Reasoning-Guided Design — end-to-end on antibody-antigen complexes. Gradients from the diffusion objective flow back through the reasoning expert. ━━━━━━━━━━━━━━━━ 📊 RESULTS ━━━━━━━━━━━━━━━━ Evaluated on simultaneous multi-CDR redesign and the RAbD CDR-H3 benchmark: ✅ Best RMSD & DockQ on RAbD — redesigned H3 loops are geometrically accurate *and* docked well ✅ Lowest backbone dihedral divergence (JSDbb) among all baselines ✅ Reduced intra- and inter-chain steric clashes ✅ Generated sequences score lower perplexity than native antibodies under IgLM & AbLang ✅ Plug-and-play: swapping the diffusion backend to UniMoMo still improves RMSD and IMP ━━━━━━━━━━━━━━━━ 💡 WHY IT MATTERS ━━━━━━━━━━━━━━━━ Proteo-R1 isn't just a better antibody design model. It's a blueprint for coupling deliberative LLM reasoning with any physical generative process — interpretable, modular, and backend-agnostic. 📄 Paper: arxiv.org/abs/2605.02937 💻 Code: github.com/smiles724/Proteo-… 🌐 Demo: smiles724.github.io/r1/ Great thanks to my wonderful collaborators Weihao Xuan, Heli Qi, @Hanqun_CAO, Heng-Jui Chang, @KKuanPang @XiangruTang Zehong Wang, @hcwww_ , @KejunYing @lupantech Chiho Im, Seungju Han, @richardxp888 @tikgiau. Also appreciate the guidance from advisors @YejinChoinka @jure @erranlli Naoto Yokoya, Masashi Sugiyama.