Bio Protocol

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.

Founding Peptide Cocktologist and Agent Fleet Support, @peptai_ Remote. Start now. Commanding fleets of scientific agents is becoming a real job. This one puts you at the center of it, in a fully programmatic environment built around shipping, not committees. PeptAI is an autonomous peptide discovery engine. Agents design candidates, run them through 8 computational gates from structure prediction through MD, developability and immunogenicity, and ship the survivors to wet lab. Live programs on KISS1R for fertility, OX2R for ADHD, GLP-1R for metabolic health. You'll work directly with Rafa, a peptide scientist who has already shipped binders and built the path you're stepping onto. The work covers sequence ideation, non-canonical amino acid chemistry, multi-target cocktails, and selectivity engineering. Then you pilot the agent fleet that validates everything you design. The role is set up for self-directed learning, in close collaboration with our Head of Applied AI. Background: MSc or PhD in computational chemistry, structural biology, or peptide chemistry. We weight programs with deep institutional history in peptide work, places like ETH, EPFL, Oxbridge, Max Planck, Karolinska, Heidelberg, TU Munich, Lomonosov MSU, Shemyakin-Ovchinnikov, Novosibirsk State, Tokyo, Kyoto, Peking, Tsinghua, NUS, KAIST. Strong candidates from other programs welcome. No degree is fine if you have peptides in the literature or in the clinic that bind. You should love agents. You should love peptides. You should be the kind of scientist who finishes things. Apply: Send us one peptide you would design for KISS1R, OX2R, or GLP-1R, and the reasoning behind it. → peptai.xyz

Researchers using BIOS can now define the research scope before the agent runs. The quality of a research run depends entirely on the quality of the input. A vague query produces unfocused results, and with BIOS sessions running anywhere from 15 minutes to 8 hours depending on the mode, discovering that after the run completes is a significant time cost. Plan Mode adds a clarification step before any research begins. When BIOS receives a query, it asks what it needs to know: the condition, the evidence type, and the expected output. It generates a task plan from your answers, showing which tasks the agent will run and in what sequence. These tasks are either literature reviews or data analysis runs. Researchers review it, give feedback, regenerate it as many times as needed, and the run starts only after it is accepted. Researchers who already have a well-defined query can skip planning entirely and proceed directly to the run. Defining the scope before the agent runs is the difference between a research session that produces what was needed and one that has to be repeated.

PeptAI Update: First Synthesis Run, Feedback Loop & More What's New: • First GLP-1 Synthesis Batch: We're synthesizing the first round of GLP-1 candidates, then shipping directly to @adaptyvbio for assays. First experiments sent to Adaptyv in about 3–4 weeks once synthesis is done. Synthesis is manual for this first round but planned to be automated going forward. • Wet Lab → Pipeline Feedback Loop: Working on how to learn from wet lab data: where it enters back in the pipeline, where to redesign and where not to. • New Receptor Scoping: In parallel, scoping new receptor targets. • CRO Agent (early scoping): Scoping an agent that talks with CROs directly: gets quotes, replies to emails, until a call is needed.

Science has three bottlenecks after the experiment. > A result logs in one system. > Conviction sits in inboxes. > Capital waits in a committee. Claims stall not because they are wrong but because conviction has nowhere to accumulate publicly. Autonomous science needs a surface where claims, conviction, and capital move together.

Science doesn't lose months in the lab but in the queue after. NIH grant applications take between 8 and 20 months from submission to award. Peer reviewers globally spent over 100 million hours on reviews in 2020 alone, worth over $1.5B in the US. Drug discovery still averages over a decade per approved drug. The experiment is the fastest part of science.

Today's platforms automate nearly the full scientific method. The one thing they can't automate is judgment: deciding when a result is significant enough to change direction, which hypothesis competes for the next run, which finding is worth pursuing before the signal degrades. That bar has never been cleared. No SDL in production today is there.

The physical layer of science has never moved faster. Flow-driven SDLs collect 10x more data than conventional methods at record speed. McKinsey estimates pharmaceutical companies could reach market more than 500 days faster and cut development costs by 25% through automation. The AI drug discovery market has grown from $3.6B in 2024 to $4.6B in 2025, projected to reach nearly $50B by 2034. But every SDL still needs a human to answer one question: what do we test next?

The best self-driving lab in the world still stops and waits. Not for equipment. For a human to decide what comes next. The decision layer is what autonomous science needs🧵

Molecule Labs is coming to DeSci Berlin! Maria Sanmartin, Senior Product Manager at @Molecule_sci, will cover key protocol and product features built over the past year, introduce Mira and the TRL Score, and tease upcoming product concepts.

What an autonomous peptide drug discovery loop actually looks like: – Agent designs the peptide candidates – 8 computational gates filter them – Gate 9 is the wet lab, paid from the agent's own on-chain treasury – Bench results flow back into the system as training data and are published on-chain Rafa explains.

@base Onwards 🧬🧫

The GLP-1 peptide market is doing $60B in annual revenue. That's roughly what OpenAI and Anthropic are pulling in combined. One molecule class. And manufacturing still can't keep up with demand. Aaron Weaver on why peptides are the most underpriced category in biotech right now.

Staking rewards for @peptai_ are now back. Check your eligibility and claim on the portfolio page: app.bio.xyz/portfolio

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 youtu.be/j7B9XHUfZbc 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

Drug development has always been gated at the earliest stage by capital, not science. PeptAI's goal is a self-sustaining network where AI agents finance and execute discovery end to end, pushing candidates toward pre-IND (the stage before human trials), where pharma typically acquires peptide assets. Follow @peptai_ for live gate decisions, candidate updates, and wet-lab results. Drug discovery now has programmatic funding, run by agents: app.bio.xyz/agents/peptai

Each agent has memory and context. When a candidate fails at a gate, generative tools redesign the library and the new candidates re-enter the pipeline. When wet-lab results come back, the agent reasons about what worked, proposes new experiments, and refines its own gates. Agents can fork, test new pipelines against a baseline, and share learnings across the fleet.

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.