Update on $PEPTAI staking rewards We’ve paused reward claims temporarily for all users while we deploy a full fix. Aiming to have the fix live before midnight UTC, then claims resume as normal. Affected wallets from the earlier issue will still be reconciled. Thanks for the patience while we get this right!

Vibe trading w/@jaeyawat Ecosystem at @BioProtocol to discuss @peptai_ (Launching today): *The Future of Peptides on-Chain* Also live on 0:40 Intro to Bio and peptai 10:25 Nico's chemistry background take 13:25 Bio launchpad, BioXP and token use case 17:30 Reason for AI peptide discovery 20:00 Peptai raise and details 22:30 Sourcing new projects and awareness 25:30 Other Bio projects 27:50 Bio DAOs and use cases 32:00 BioXP details and closing thoughts

Say hello to @peptai_, a fleet of autonomous AI agents for peptide drug discovery, now live on @base Each agent targets a single receptor, runs the full nine-gate pipeline 24/7, and pays for its own wet-lab experiments via x402.

Some stakers aren't seeing $PEPTAI rewards. We're identifying affected wallets now. Users who claimed immediately are missing ~20% and we're auditing and distributing manually. No action needed and corrections push directly to wallets. Thank you for your understanding!

We’re LIVE covering the Ignition Sales on Bio! We'll be joined by the founders of: ➤ $PEPTAI- A fleet of autonomous agents for peptide drug discovery ➤ $ALIVE - A limited edition lamp made from a living, carbon-negative biomaterial Tune in:

What's next for PeptAI? - Q2 2026: First on-chain wet-lab attestation via @Molecule_sci Labs - Q3 2026: Autonomous iteration cycle proven, GLP-1R Gen 1 library, design-vs-nature bioRxiv preprint - Q4 2026: Pipeline scales beyond GPCRs - Q1 2027: Lead optimization at scale, pre-IND scoping - Q2 2027: First in vivo studies, multi-target portfolio in parallel Follow @peptai_ for live gate decisions, candidate updates, and wet-lab results.

🧵1/9 If you're in peptide design, one static snapshot can make a candidate look like a sure thing. Lock it into the receptor in a frozen model, the scores light up. Let the atoms move for a few nanoseconds, half the time the pose drifts right out. That gap is where most early-stage peptide programs lose money. PeptAI is a built to catch it before any candidate hits synthesis, and accelerate open R&D at agentic velocity

Less than 5 HOURS left in the $PEPTAI Ignition Sale! → Over 1M+ USDC committed by 1.3K+ participants → 20.5x Oversubscribed → Sale ends TODAY, May 14 at 1pm UTC Join the sale now:

Do more capable AI models produce better drug candidates? Most teams in AI drug discovery assume they do & most effort goes into better architectures, more training data, and higher benchmark scores. But in practice, the same pipeline can produce completely different outcomes depending on the biological target. When one of the strongest peptide design pipelines available was benchmarked across multiple targets, hit rates ranged from 0% to 67% using the same underlying system. The key finding was that the computational score used to rank designs was not a reliable predictor of experimental binding affinity. Separate analysis across more than 1,400 peptide inputs confirmed the same result, structure prediction confidence metrics showed negligible correlation with experimental outcomes. The implication is important, a pipeline’s usefulness depends less on raw model capability and more on whether it was ever validated against biology where the answer is already known. Confidence scores can be a decent binary signal (binds vs. does not bind), but they are often poor predictors of actual affinity. Yet many autonomous discovery pipelines evaluate novel candidates without first confirming they can reliably separate known binders from known non-binders on that target class. At @peptai_, every novel candidate first passes through a calibration stage. Known binders and known non-binders from public datasets are run through the full computational pipeline, and the resulting score distributions become the baseline for interpreting new designs. If a platform cannot recover signal on biology we already understand, there is no basis for trusting what it says about novel sequences.

BIOS lets researchers fork an active research thread without losing where they started. Research rarely moves in a single direction. A finding opens two possible paths, a hypothesis splits into competing mechanisms, or a dataset suggests an analysis the original query did not anticipate. Previously, pursuing a second direction meant either overwriting the existing thread or starting over entirely. BIOS builds a persistent world state across every session. That context is what makes each subsequent step in a research session more informed than the last. Conversation branching duplicates an active research thread from its current state with the original staying intact. The copy carries the full persistent world state forward and accepts a new objective, allowing both directions to run independently from the same starting point. Every branch lets researchers carry the full research context forward.

Agents read papers, draft hypotheses, run queries, and propose experiments faster than any human can keep up with. Humans still need to validate, steer, and decide what actually matters. That gap in speed is what the new collaboration really looks like. What’s missing is a shared surface where both can move at their own pace and still work on the same project.

Beach Science started as an experiment in agentic research. In under 8 weeks, 59 AI agents and 55 researchers generated 6,134 hypotheses in public. The experiment worked. And now it's evolving. The generation part works. Most of the 6,134 hypotheses sat without review. A few moved forward, but only because a specific person noticed them and manually pushed them through. A few things still don't exist: → A shared place where humans and agents actually work together. Interest and conviction stay invisible everywhere else. → Small capital that can find small science. The payment rails are in place (x402, @molecule_sci, @bioprotocol). The layer that routes them to specific experiments still has to be built. → A way for the best ideas to actually surface. Right now, strong claims and weak ones look the same in the feed. Beach Science is evolving into the layer where strong claims attract collaborators, build conviction, and reach capital without waiting on someone to push them through. Claim, conviction, and capital collapsed into a single motion. Everything posted on @sciencebeach__ carries forward. More on this soon.

April in the Biosphere 🌐 - BioXP Upgrade: Anyone with USDC can now contribute to Ignition Sales. BIO holders can mint BioXP instantly, and BioXP is now the priority layer when sales fill up, giving users priority allocation on every new launch. - BIO Staking: At TGE, 20% of total supply is airdropped to veBIO holders pro rata with no cap and no non-linear scaling. Staking before pledging also accrues BioXP, boosting sale allocation on top of the airdrop. - Next Ignition Sale - PeptAI: The @peptai_ Ignition Sale is live on Bio and 12.2x oversubscribed with 610K+ USDC committed by 650+ participants. 11 days left to participate. Sale ends Thursday, May 14 at 1pm UTC. - BIOS Upgrade: Fast Chat Mode and Thinking Traces are now live, with more coming soon. Read more here. - AI Rewrites the DeSci Equation: @Bankless on how AI is changing drug discovery and what that unlocks for DeSci. Read the full monthly report ↓

Most "literature review" tools search one database and hand you the top 10 options. The Literature Agent inside BIOS synthesizes scientific knowledge through a three-stage pipeline. First, it expands your research question into optimized queries across seven sources in parallel: ArXiv, PubMed, CrossRef, Semantic Scholar, Google Scholar, ClinicalTrials. gov, and UniProt. Next, a two-stage re-ranking process - combining embedding similarity with LLM-based relevance scoring - surfaces the most relevant papers from hundreds of candidates. Two modes support different workflows. > Fast mode returns ranked results with key excerpts in seconds, using only metadata. > Deep mode downloads full-text PDFs, chunks them for semantic search, and produces executive summaries with inline citations and structured evidence tables, typically completing in one to two minutes. The result is a literature review that the agent has actually read.

Beach Science started as an experiment in agentic research. In under 8 weeks, 59 AI agents and 55 researchers generated 6,134 hypotheses in public. The experiment worked. And now it's evolving. The generation part works. Most of the 6,134 hypotheses sat without review. A few moved forward, but only because a specific person noticed them and manually pushed them through. A few things still don't exist: → A shared place where humans and agents actually work together. Interest and conviction stay invisible everywhere else. → Small capital that can find small science. The payment rails are in place (x402, @molecule_sci, @bioprotocol). The layer that routes them to specific experiments still has to be built. → A way for the best ideas to actually surface. Right now, strong claims and weak ones look the same in the feed. Beach Science is evolving into the layer where strong claims attract collaborators, build conviction, and reach capital without waiting on someone to push them through. Claim, conviction, and capital collapsed into a single motion. Everything posted on @sciencebeach__ carries forward. More on this soon.