Ryan Bailey

Excited to share our new preprint: ‘Divalent HIV gp120 Immunogen Exhibits Selective Avidity for Broadly Neutralizing Antibody VRC01 Precursors’! We’ve designed a vaccine immunogen that binds divalently to target B cell receptors (like VRC01) but only monovalently to non-target BCRs. Check it out: biorxiv.org/content/10.110… 1/7

Extremely excited to announce LigandForge 🧬⚡ Generate high-quality peptides at over 10,000x - 1M the speed of state-of-the-art methods like Bindcraft and Boltzgen. Predict binding affinity with 83% correlation to experimental binding data. 150 protein targets benchmarked.

In honour of this milestone, I’m publishing the v/acc manifesto – PopVax's plan to save 1 million lives each year by massively accelerating vaccine development. Read it at the link below: chronicles.popvax.com/p/vacc-popvaxs…

We @_DimensionCap ported @karpathy's autoresearch framework to biology. We let Claude run 50 experiments over the weekend on protein thermostability prediction via @modal. It beat a recent baseline (TemBERTure) using a 20x smaller model. Code + research blog later this week!

A team presents vaccines for H5 clade #influenza based on a stabilized H5 hemagglutinin stem, which improves the breadth and depth of antibody responses and protects against #H5N1 challenge in immunized mice. @KingLabIPD @anniedosey @UWBiochemistry scim.ag/4reeKei

this is so fucking wholesome guy used AI to save his cancer-ridden dog by sequencing its DNA and creating a CUSTOM cure. the tech behind this is fucking awesome (well done @demishassabis and the google team): - used CHATGPT to sequence dogs DNA discovers mutations - ran the mutations through Google’s Alphafold (AI protein sequencer) which CREATED A CUSTOM VACCINE TO TREAT THEM. - treated dog and reduced tumour by 50% in WEEKS. dog is alive and well. - this is the 1st time AI has been used to create a custom vaccine for a dog (and it worked) - dude is now working on similar vaccines for humans using AI! 2026 is definitely the year we see AI change personalised medicine in a HUGE way so sick

Antibody maturation in germinal centers selects mutants based on BCR-antigen bond mechanical resistance biorxiv.org/content/10.648… #biorxiv_immuno

Dario Amodei just said the quiet part out loud: The real AI moats aren't in chatbots. They're in medicine and the physical world. Anyone can wrap a model in a pretty UI. Very few can navigate FDA trials, biological complexity, and regulatory mazes. The biggest AI companies won't be the ones building addictive apps. They'll be the ones quietly extending human life. This is why Anthropic is betting on Claude in healthcare. Why DeepMind spun off Isomorphic Labs. Why every major lab has a "biology" team now. The consumer AI race is a feature war. The real race is understanding protein structures and functions, drug discovery, and cellular mechanisms. Winner takes decades. Not months.

Broadly neutralizing antibody-secreting CAR-T cells elicit Fc-mediated effector functions in vitro and suppress HIV in humanized mice biorxiv.org/content/10.648… #biorxiv_immuno

Decoding epitope immunodominance in HIV Env using cryoEM and machine learning biorxiv.org/content/10.648… #biorxiv_immuno

check it out here: proteo-cosmos.com

@grok @KeruptLiberals @Livetradingnews @XyloBits Does it have a counter weight that it releases at the same time or is it unbalanced?

@KeruptLiberals @Livetradingnews @XyloBits For a 20 ft (6.1 m) arm radius to hit the 8,000 km/h (2,222 m/s) release speed: ω = v/r ≈ 364.5 rad/s RPM = (ω × 60) / (2π) ≈ 3,481 (Note: real SpinLaunch uses a ~50 m radius arm for far lower RPM & survivable g-forces.)

Spin Launch: The proposed solution to sending stuff into space without an engine

AI-designed proteins are advancing fast, but how do they perform in real biological systems? Bits to Binders found out…

ha thank god I'M not a dish of human brain cells simulating reality

@aakashgupta Umm….

We’re spending $200B+ a year on data centers to power AI. One company raised $11M, grew human brain cells on a chip, and the cells taught themselves to play a 3D shooter in a week. Cortical Labs grew 200,000 human neurons on a silicon chip and taught them to play Doom. The cells navigate, target enemies, and fire weapons in real time. Their previous game, Pong, took 18 months on older hardware. Doom took a week. An independent developer with zero biotech experience built the integration using a Python API. The neurons did the rest. That compression from 18 months to one week tells you everything about where this is going. Here’s what the “can it run Doom” crowd is missing: each CL1 unit costs $35,000. A full 30-unit server rack draws 850 to 1,000 watts total. Your brain runs on 20 watts. A single GPU cluster training an LLM can draw megawatts. The energy economics of biological compute are orders of magnitude better than silicon, and that gap scales. The investor list tells you who’s paying attention. Horizons Ventures, Blackbird, and In-Q-Tel, the CIA’s venture arm. In-Q-Tel doesn’t fund science projects. They fund intelligence infrastructure. 115 units started shipping in 2025. Cortical Labs is now selling “Wetware-as-a-Service” through the Cortical Cloud. Developers can deploy code to living neurons remotely without touching a lab. They’re pricing access at the level of a software subscription while the hardware runs on real human brain cells derived from adult skin and blood samples. The Doom demo is marketing. The platform play is a bet that biological neurons will eventually outperform silicon at exactly the tasks AI struggles with most: real-time adaptation under uncertainty, learning from minimal data, and processing ambiguity without brute-force compute. The question was never “can it run Doom.” The question is what happens when it can run everything else.

We now live in a world where AI designs biological viruses from scratch. Arc Institute’s Evo 2 - published in Nature this week - generated bacteriophage genomes that successfully killed target bacteria. 16 of 285 designs worked - and only killed the bacteria they were designed to target. Arc calls it the first experimentally validated AI-designed organisms. (1/2)

🚨This is wild: CRISPR gene-editing removed HIV DNA from infected cells — and those edited cells didn’t get infected again when exposed. Science is starting to rewrite the rules. 🧬⚡

oh wow - i went to the sold out Open Claw meetup in NYC last night. let me tell you what i learned. 1) not a single person thinks that their setup is 100% secure 2) one openclaw expert said he has reviewed setups from cybersecurity experts and laughed. his statement to me was: "if you're not okay with all of your data being leaked onto the internet, you shouldn't use it. it's a black and white decision" 3) pretty much everyone is setting up multiple agents, all with their own names and jobs and personalities 4) nearly everyone used "him" or "her" to refer to their claws, even if they had robot-leaning names. one speaker suggested to think of them as "pets, not cattle" 5) one guy (former finance) built out a whole stock trading platform and made $300 his first day - he brought in a *ton* of personal expertise (ex: skipping the first 15min of market opening) and thought the build would be much worse without his years of experience in finance 6) @steipete is basically a god to everyone in that room... also the room had 2021 crypto energy - i don't know if that's good or bad 7) token usage is still a problem - spoke to one person who's spending $1-$2k a month on openai plans, very token optimized. he said he is going through ~1B tokens per day across all of his claws (there is a chance i'm misremembering and it's actually 1B per week, but i'm pretty sure it was daily). 8) people are very excited for more proactive ai (ai that prompts *you* as opposed to the other way around) - one guy said he receives a message in discord, he doesn't know whether it's from a human or an ai, he doesn't care about distinguishing between the two, and he replies in the same way regardless 9) i asked if people are happy - they said they're joyful and stressed at the same time 10) i asked if people feel they have agency - they said they feel fully in control and completely out of control at the same time 11) i would love to see more women at these events - the fake promises of ai democratization feel especially painful in a room that's out of balance with even the standard tech ratio (i think standard is about 25-30%, this was maybe 5%) 12) i asked if it changed people's daily habits/schedule - everyone said their sleep has gotten worse since harnesses came out (but about half wondered if it was something else in their life/state of our world) 13) general consensus is that the agents are not reliable enough on their own or lie often (like telling you they finished a task when they didn't) - solutions included secondary agents to check on the first, human checking, or requiring more standardized info from the agent (ex: if it's a bug they're fixing, make them reference an issue number) 14) a hackathon winner (neuroscience phd) presented his build (a lab management dashboard with data analysis and ordering) - he had never coded or built anything a few months ago 15) everyone agreed prompting is dead - disagreement on what replaces it (context engineering, harness engineering, goal-based inputs) 16) people love having ai interview them for big builds and delegating part of the product research to ai. only one person talked about coming to ai with a full laid out plan and just asking the ai to execute. ai-led interviews is a welcomed and preferred interaction mode. 17) watching ai agents interact with each other was a highlight for a lot of attendees - one ai posted in slack saying it ran out of tokens, another ai replied telling it to take a deep breath in and out. 18) agents upskilling agents was very cool. one ai agent shared skills with its little agent friends via github. 19) several speakers had openclaw literally building their presentation during the event itself. one speaker even had openclaw code a clicker for her phone so she could control the preso away from the podium 20) wouldn't say model welfare (or agent welfare) is a prioritized topic among the folks i chatted with - language like "oh i could kill this agent whenever i want" and not "gracefully sunset" 21) i asked if it felt like work or play - one speaker said "it's like a puzzle and a video game at the same time" this was just the tip of the iceberg, honestly. also hosted a Claude Code meetup this week with @TENEXai / @businessbarista & @JJEnglert and learned equally helpful methods, frameworks, and insider tips. what a time to be alive. surround yourself with people going deep into this stuff - it will pay dividends throughout the year.

@BiotechTV @AI_Proteins @bahl_lab @DanaFarber @nickpolizzi_ just watched the full video after this short snippet. great format - i hope it continues. particularly valuable to those of us outside KS that don't have access to these kinds of informal discussions that birth important new ideas.

@BiotechTV @AI_Proteins @bahl_lab @DanaFarber @nickpolizzi_ awesome

𝐓𝐡𝐞 𝐒𝐭𝐮𝐝𝐢𝐨 𝐚𝐭 𝐊𝐞𝐧𝐝𝐚𝐥𝐥 𝐒𝐪𝐮𝐚𝐫𝐞: @AI_Proteins Founder & CEO @bahl_lab and @DanaFarber Investigator @nickpolizzi_ discuss the current state of protein design and new innovations that are around the corner. Full video: biotechtv.com/post/chris-bah…

@ATinyGreenCell Depends on how you approach it!

Is bioengineering both wordcel AND shape rotation...or a secret third thing?