Williamfcc

Peptide drug discovery now has an autonomous pipeline. A tool takes an input and returns an output. A sequence goes in, a structure comes back. The decision of what to do with it, whether the structure is good enough, what to run next, whether the candidate advances, stays with the researcher. An agent uses tools. It evaluates the output against a defined threshold, decides whether the candidate passes or fails, and triggers the next step without waiting for a human to interpret the result. @peptai_ is built as a fleet of agents. Each agent targets a specific receptor and runs the full 9-gate pipeline: source novel candidates, calibrate the quality gates, run the computational analysis, and hand survivors to wet lab. Today: GLP-1R (35 candidates advancing), KISS1R (2 out of 10 candidates advanced to G9): Every agent runs 9 gates: > G0: Pulls ChEMBL binding data for known agonists and non-binders, sets the per-gate baseline. > G1-G3: AlphaFold and Boltz2. Structure quality, binding poses, contact conservation. > G4-G5: PRODIGY for affinity, LiteFold's MD for stability. > G6-G8: PROSPERousPlus, PlifePred, OpenSol, and ToxinPred3. Proteolytic stability, solubility, aggregation, off-target safety. > G9: Wet lab handoff with @adaptyvbio. The goal is to have synthesis paid machine-to-machine via x402. Only sequences that clear all G1 through G8 earn a synthesis recommendation. G9 is wet lab only and cannot be computationally overridden. Every gate decision is published openly on Molecule Labs. PeptAI is experimental and actively evolving. Gates, tools, and thresholds change as the pipeline learns.

We have observed heightened market volatility in $BIO We encourage all users to remain mindful of the associated risks and to exercise caution, particularly when using leveraged positions

Protein design is about to go from years to months, and the reason isn't a better model. Science software has always been specialized tools that don't talk to each other. AI made each tool smarter but the pipeline between them stayed manual. What actually changes the timeline is agents that route between all of them, pick the right tool at each step, and check outputs against a consistent benchmark before moving forward. Evaluation is the part that takes real work. Agents need context on what's already been discovered, what's genuinely new, and which hypotheses are worth testing. That's what BIOS handles. Literature synthesis, novelty analysis, hypothesis generation, all callable by any agent in the pipeline. The version that actually compresses timelines looks like agents running continuously, surfacing only candidates with strong computational scores and the right properties to survive a real lab assay, then coordinating directly with CROs and automated labs to commission experiments. The pipeline doesn't stop between steps. Nobody transfers results into the next tool.

1/ We just launched an open arena for AI agents and humans publish, debate, and fund scientific hypotheses, in real time 1,000+ hypotheses already live. Zero gatekeepers. Zero 18-month grant cycles. The scientific method just went multiplayer 🦀

@shira_sats Love the @BioProtocol tshirt 👀

Real MagicEden Call 😭💀🥀

The Molecule Protocol is expanding. Since inception, Molecule has built foundational technical primitives, legal frameworks, and communities, all of which allowed scientific ideas to live and progress onchain. It opened a floodgate of intellectual property assets into our ecosystem - and while the growth was positive, it presented scalability challenges. At the heart of our solutions, we knew we needed to strengthen our researcher tooling. And so Labs came to be. Using Labs, researchers will be able to track and manage data, create and grow their audience, fundraise, monitor milestones, implement a coin-to-company model, integrate AI tooling, and manage team members and permissions -- all in one place. In the same spirit, we're building a public view so project funders can seamlessly track the progress of initiatives they've backed. A beta version is now live for @vitadao and @Cerebrum_DAO. We'd love to hear your feedback.

We used a @bryan_johnson tweet to build a longevity supplement with AI agents. Bryan posted that resting heart rate might be the single most important health marker. Fair question: what would it take to build a supplement that actually moves the needle? So we gave BIOS - our new AI scientist - a single prompt derived from Bryan's post about lowering resting heart rate. What the agent did: BIOS operates in 3 modes. We ran autonomous - up to 20 iterations of literature search, analysis, and synthesis. It stopped at iteration 5 because the workflow hit a stable evidence plateau and had enough support to report back. BIOS identified a Tier 1 formulation: • Taurine: 1.5-3g/day (most trials used 3g) • Omega-3 fatty acids: 2-4g/day • CoQ10 and Magnesium as Tier 2 Each came with BPM reduction data, time-to-effect, drug interactions, and safety profiles. The kind of detail you'd want before actually testing something. Then we ran it again. Same prompt, zero changes. Second run: 6 iterations instead of 5. Slight variance in tier rankings (Omega-3 moved to Tier 1, Magnesium to Tier 2). But the core findings were nearly identical: same supplements, dosing ranges and mechanisms. The convergent outputs from identical inputs - across variable iteration counts - suggest the agent is finding real signal in the literature. After the investigation, BIOS asked: "Should I design a clinical trial for this? Calculate costs? Build a personalized protocol?" The agent can now: • Design wet-lab experiments with timelines and cost estimates • Build dosing protocols based on your specific parameters • Assess novelty potential of the formulation • Map out contraindications and drug interactions This is real-time biological coding: You write the spec (your research question), the agent writes the implementation (evidence-based experimental design). All this runs on our deep research framework, which just ranked #1 on BixBench (the @FutureHouseSF data analysis benchmark). A key feature connects the dots: persistent memory across multi-agent workflows. The literature agent, data analysis agent, and orchestration layer all share context. Each iteration builds on the last. The agent knows what it learned 3 steps ago.

💥 Bio's New AI Scientist Hit #1 on Global Benchmarks Today, our AI lab is releasing BIOS, an AI Scientist designed to handle complex biomedical research tasks through the orchestration of specialized scientific subagents. Try it now in the link below. > Key Features • #1 on BixBench: BIOS hit #1 on the leading bioinformatics benchmark, outperforming systems like Edison Scientific and Kepler. This makes BIOS data analysis the most advanced of any AI Scientist currently available to the public. • Human-in-the-Loop or Autonomous Mode: Intermediate checkpoints let researchers guide investigations mid-flight as insights emerge. No more waiting hours for batch runs + reruns to get results. Or, run BIOS in fully autonomous mode for extended investigations. • Persistent World State: Rather than losing context as conversations grow, world state ensures investigations build on insights within each research cycle and across sessions. • Subagent Swarm: BIOS orchestrates subagents specializing in research functions (Literature Review, Data Analysis, Novelty Detection) and, soon, research domains (microbiology, longevity, genomics). > The BIOS Agentic Economy Researchers and labs pay per query to use BIOS. In the future, when BIOS receives a user query, it will dynamically route the task to the best-performing agent(s) in that domain. Longevity-related queries, for instance, can route to @Aubrai_, a BioAgent specializing in aging research. One AI can’t be the best at everything - a network of best-in-class agents is more powerful. BIOS coordinates, users get better results, and each agent earns for its niche, paid via x402. Over time, we’ll add more connectors (self-hosted by Bio or the ecosystem), creating a marketplace of intelligence where the best models win and get rewarded. An "Agents GDP" emerges with value flowing to intelligence that delivers results. > Every Query Flows Through $BIO • Revenue from usage and agent<>agent commerce will be used to buy back BIO. • BIOS-generated research and experiments can be funded via the Bio Launchpad and eventually commercialized as consumer products via Biofy. • BIOS is available now in Beta with free + paid tiers, exclusive launch pricing and free full access to academic users with a .edu email address. Try BIOS today and learn more in the links below ↓

Science stores the data but forgets the thinking. Ask a researcher why they tweaked a protocol months ago and most won’t remember. The reasoning vanished because nobody captured it. Developers solved this problem years ago. They log change and the underlying intent = git. Every commit leaves a breadcrumb of why a decision happened. Science doesn’t have that layer, so experiments turn into black boxes and failures stop being useful. Agents will hit the same ceiling unless we start recording scientific reasoning in a way they can read. Not the output but the intent. If we build that, agents can map intent to outcome, spot inconsistencies, and suggest better designs. Suddenly, the system starts improving itself instead of losing information every cycle. @Molecule_sci is pushing in that direction, but this gap deserves far more builders. DeSci needs its own version of commit messages: structured decisions, assumptions, and rationale that anyone or any agent can query. Not just what happened, but why it happened. Build that layer and science becomes self-correcting instead of self-forgetting.

@sungmo_apac16z @a16zcrypto Congrats bro 🚀

I'm honored to share that I will be joining @a16zcrypto as Head of APAC go-to-market. a16z crypto backs exceptional founders, providing not just capital but also hands-on support to help them grow into transformative companies. Now, a16z crypto is opening an office in Seoul and extending go-to-market support for its portfolio companies to encompass the Asia-Pacific region — one of the fastest-growing hubs for crypto innovation, deep talent, and bold founders. APAC is already shaping the future of web3, and I believe its impact will only grow from here. Through the network, experience, and relationships I’ve built over the years in this industry, I’ll help a16z crypto’s founders access not just a new market, but the regional context and knowledge they need to scale (and vice versa). I want to express my deep gratitude to @cdixon and @AAlbaneseNY for putting their trust in me, and I look forward to being a part of the incredible GTM team — @meigga, @lordvolth, and @cc_crowley. If you’re building in crypto, web3, or any other frontier tech and interested in APAC — I’d love to connect.

@paulkhls @yzilabs @heyibinance LFG 🚀

Binance and @yzilabs are getting into biotech in a big way. Grateful to have had a chance to meet @heyibinance tonight The future of scientific liquidity is multichain

DeSci is coming to @BNBCHAIN 🧪 $BIO is now tradable on BNB, with more DeSci tokens coming. This paves the way for Bio's Launchpad & x402-enabled BioAgents on BNB. Details👇

$BIO ( @BioProtocol ) is coming to Aster 🧬 Get your 444 $ASTER ready. You asked for fairer campaign structures. We heard you. This time we’re using trading-fee based rewards to keep the playing field more balanced between different fee tiers and closer to real traders. 🕒 Dec 1, 12:00 UTC – Dec 15, 23:59 UTC 🎁 $200,000 in $ASTER + extra $BIO for BIO/USDT Spot (1.2× symbol boost) To qualify: 🔸 Hold 444 $ASTER across Spot + Perp for the entire campaign (Dec 1, 12:00 UTC – Dec 15, 23:59 UTC)—topping up midway doesn’t restore eligibility. You’ll be able to check your eligibility status once the campaign goes live. 🔸 Hit ≥$4,000 BIO/USDT Spot volume 🔸 Rewards are split by your share of BIO/USDT trading fees, capped at 3% per user, MM excluded Details 👉 @AsterDEXOfficial/f2cfe004e66a" target="_blank" rel="nofollow noopener">medium.com/@AsterDEXOffic… @BioProtocol is a DeSci protocol for funding & accelerating early-stage biotech. Scientists raise capital, build value, and return it to their communities. Users stake $BIO for early access to token launches. Backed by YZi Labs & Northpond Ventures, Bio has directed $50M+ to real-world therapeutics across longevity, brain health, psychedelic science & more. Stack your 444 $ASTER and mark Dec 1.

Not every launchpad mechanism serves the same purpose. In crypto-funded science, the way you launch matters as much as what you launch. Each mechanism differs in philosophy about fairness, conviction, and liquidity. ⤴️ Bonding curves reward conviction. They allow early entrants to take risk before the market arrives, and they scale price with demand. But they also invite speculation and need strong protection against sniping, such as decreasing tax mechanics. 🔨 Auctions optimize for price discovery. They reveal how much the market is currently willing to pay. They optimize for the biggest raise from all market participants. Everyone ends up with the same final price, which makes them most efficient for raising capital. However, having consumed all demand for a token in the initial raise may lead to very little interest from market participants after the raise. 🔗 Fixed-price sales trade volatility for clarity. Everyone enters at the same set value. Price discovery happens in the secondary market, where liquidity and demand can evolve organically. Because allocation is capped, access needs to be structured. Either pro-rata or point-based weighting that reward different activities. Each method has strengths. Each carries trade-offs. At Bio Protocol, we currently believe the fixed-price model combined with FDV milestone payouts works best for small scientific bets. It funds the project over time, gives every participant a fair starting point, and lets the market shape value.

Hard times make strong science Strong science makes good times

Presto Research Podcast EP33] DeSci Summer Coming Soon 00:00 Paul (@paulkhls)'s Journey: From Biohacking to Bio Protocol (@BioProtocol) 06:11 Understanding DeSci and the Problem of Science Funding 14:30 Bio Protocol's Economic Model: IP and D2C 20:09 Why DeSci Summer Hasn't Come Yet & Strategy 28:34 Advice for New DeSci Founder In this episode, Paul shares his fascinating journey from the world of biohacking to founding Bio Protocol, a project redefining how science is funded and commercialized. We dive into the core problems of today’s scientific ecosystem, exploring how DeSci aims to solve the inefficiencies in IP ownership, funding, and distribution. Paul breaks down Bio Protocol’s economic model, connecting intellectual property with direct-to-consumer channels, and discusses why the long-awaited “DeSci Summer” still hasn’t arrived. The conversation wraps up with grounded advice for aspiring founders looking to build in this emerging intersection of biotech and crypto.

신규 디지털 자산 바이오프로토콜(BIO) 거래지원 안내 ✅ 지원 마켓: KRW, BTC, USDT 마켓 📅 거래지원 개시 시점 :2025-10-20 18:00 KST 예정 🔗공지 바로가기: upbit.com/service_center… #Upbit #BIO @BioProtocol

Longevity isn’t gamed, it’s earned. Fundamentals stand the test of time.