
obvious ventures | investor: @amilabs @inceptivecom @radicalnumerics @inductive_bio @boltz_bio @ataraxis_ai @syntenyAI and several more
Joined November 2025
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- Following893
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rohan ganesh retweeted
AI x bio has had an insane week.
> Radical Numerics: Omnii-1 designed phages given to a human patient
> Claude: 17 scorpion-toxin binders validated in the lab
> OpenAI: acquired Orchestra Bio
> OpenAI + PerturbAI: agents found new neurodegeneration leads in 32 minutes
> RFD4-Proteina: open programmable protein design model
> BioEmu2: new biomolecular simulation model
> Chaperone: agent framework connecting 50+ protein design tools
> Xaira: launched X-Scientist for agentic drug discovery
> Sophont: Holon-S1 predicts gene activity from pathology slides
> Boltz + BMS: bringing biomolecular models into pharma workflows
> Phylo + Bayer: deploying Biomni Lab across research teams
> Kevin Weil, ex-OpenAI, launches Warp Sciences: AI + robotic labs for drug discovery
> Carbon-X: open DNA model its team says matches GPT-6.1 Sol at ~1,000x lower cost
This is happening across the whole stack: models, protein design, simulation, agents, robotic labs, pathology, pharma workflows, and actual patient use. Bio is becoming computer.
rohan ganesh retweeted
1/10 Introducing Omnii Genome, one of eight Advances built on Omnii-1.
A single generalist model now outperforms leading specialized models (AlphaGenome) in linking genetic changes to gene activity and mapping regulatory interactions.
These capabilities help make sense of the 98% of the human genome that doesn’t code for proteins
How Omnii-1 works:
rohan ganesh retweeted
ai x bio just had a ridiculous 48 hours:
- Omnii-1: AI-designed phage given to a patient
- Claude designed scorpion toxin binders
- OpenAI acquires Orchestra Bio
- Baker Lab: RFD4-Proteina
- Chaperone: agent framework for protein design
- BioEmu2: protein simulations
- Artios: enzyme design model
- OpenAI + PerturbAI: agents find brain disease clues
- Xaira X-Scientist
anything missing?
rohan ganesh retweeted
Introducing Omnii-1, our most powerful genome language model, and a major step toward general biological intelligence
In a world first, Omnii-1 designed the first AI-generated bacteriophages to be administered to a human patient with an antibiotic-resistant infection under compassionate use
rohan ganesh retweeted
I'm thrilled to welcome Jill Hruby @jill_hruby and Paul Friedrichs as biodefense advisors to @RadicalNumerics!
Jill formerly led the National Nuclear Security Administration, and Paul served as the inaugural White House Director of Pandemic Preparedness and Response.
As AI opens new frontiers in biology, strengthening our ability to defend against biological threats is more important than ever.
We're incredibly fortunate to learn from their experience and leadership as we advance this mission.
Welcome aboard, Jill and Paul!
Read the announcement:
radicalnumerics.ai/blog/biod…
rohan ganesh retweeted
Multimodal control and generative design is in our DNA, across all domains, from biology to images
Very excited to share this work in early-fusion training
We're building a new kind of AI lab. Check out the work by @keshikiayame @armin @MichaelPoli6
1/8 Does text-to-image generation need language pretraining?
Today, we share Halcyon, a family of early-fusion, base models trained from scratch. Halcyon outperforms strong late-fusion baselines with up to 275× less pretraining compute. Models and recipes are open.
Halcyon is part of a research program at Radical Numerics on the science of multimodal pretraining and scaling.
rohan ganesh retweeted
New research on pretraining data mixing in biology.
At Radical we're huge supporters of driving frontier research in AI and sharing it with the community.
So much cool work at the intersection of biology, this is the next frontier of AI 🧬🔥📈
rohan ganesh retweeted
I'm excited to announce that Christine Parthemore @clparthemore has joined @RadicalNumerics as head of biodefense.
At Radical, we have a dual-mandate to drive the frontier of biological AI for both design *and* defense. Christine will lead the charge on making the best of our tech available to the biodefense community.
Christine was most recently head of the Council on Strategic Risk, and has been a pillar of the biodefense community in the US, Europe and Asia.
We're excited for her to drive our partnerships with allies around the world.
More on Christine's thoughts here:
radicalnumerics.ai/blog/chri…
rohan ganesh retweeted
We’re hiring across @RadicalNumerics
Not just MTS this time
We’re looking for:
→ Head of BD
→ Technical Recruiter
→ People Manager
→ Business Associate
→ Marketing
and 14 other open roles
If you want to help build the company behind the next generation of biological AI, come join us
radicalnumerics.ai/join-us
rohan ganesh retweeted
Get the stories behind the models.
@inductive_bio cofounder @benbirnbaum talks about ADMET modeling, the “whack-a-mole” of compound optimization, and why trust is earned through transparency. benchling.com/blog/how-induc…
rohan ganesh retweeted
I sat down with Latent Space to talk about the research preview of Omnii, our latest genome language model, and what becomes possible when AI can reason more directly across biology.
We covered a lot of ground, especially the potential across human health.
Much more to share in the coming months.
youtube.com/watch?v=B7DdNj_V…
SF VC'S have found a new watering hole in Jackson Square. 37+ firms in about six blocks.
Fun fact: These are 170 year old gold rush brick buildings and @ThriveCapital paid $9M for a firehouse at 451 Pacific.
rohan ganesh retweeted
“Human dose is the right reward”
Worth a read from the @inductive_bio team. Such an interesting case study in how a smart AI team studied the workflows of real world med chemists to learn what they were actually optimizing for, at the end of the day. They realized that chemistry teams were rarely able to explicitly optimize out the gate for the right reward with prior methods because the reward is complicated, messy, and multivariate.
Accurate human dose prediction is the right reward.
Today @inductive_bio is launching Beacon-2, which can predict human dose directly from chemical structure.
A dirty secret of AI for drug discovery is that most systems optimize the wrong reward, making them near useless in practice. Human dose is the right reward – it’s what every drug program is already optimizing. Making it computable will unlock the real-world impact of agentic drug discovery.
Beacon-2 builds on our Beacon-1 models that beat 750+ competitors to win all three OpenADMET blind challenges. Whereas Beacon-1 provided a suite of models for individual molecular properties, Beacon-2 is a single system that predicts human dose end-to-end.
It has three configurable modules: an ADMET-PK foundation model, a potency module that can use our fine-tunable co-folding affinity model or physics-based methods like FEP, and a PK/PD model with physiologically-informed mechanistic models of pharmacokinetics.
We share validation results from Beacon-2 in a blog we are releasing today. We show that across 20 real-world drug programs running through Inductive, dose predictions from Beacon-2 agree with those from experimental data with a Spearman’s ρ of 0.61. And in a similar study on 325 publicly available compounds from the ExpansionRx OpenADMET competition, we show a Spearman ρ of 0.79.
As a proof-of-concept, we gave Beacon-2 to Indy, our medicinal chemistry agent, which used it over 5 autonomous design cycles on a recently disclosed SARS-CoV-2 program to cut predicted human dose 17x, which can be the difference between a drug program that gets killed and that makes it to clinical trials.
We compared the compounds that Indy designed using Beacon-2 against compounds from an optimization loop that used a potency-only reward function. The Beacon-2 compounds were favorable across three independent measures: they were preferred by chemists in blinded comparisons 87% of the time; were more druglike according to QED; and had a lower projected dose when computed by alternate methods relying on in vivo rat models and allometric scaling. Based on these encouraging results, we are now running prospective studies where compounds optimized by Beacon-2 will be validated in the lab.
Read the full post here: inductive.bio/news/beacon-2
At Inductive, our long-term goal is to build superhuman chemical intelligence, and by completing the reward function for autonomous drug discovery, Beacon-2 is an exciting step toward that goal.
Reach out if you’d like to get involved.
Inductive is cooking!
Today @inductive_bio is launching Beacon-2, which can predict human dose directly from chemical structure.
A dirty secret of AI for drug discovery is that most systems optimize the wrong reward, making them near useless in practice. Human dose is the right reward – it’s what every drug program is already optimizing. Making it computable will unlock the real-world impact of agentic drug discovery.
Beacon-2 builds on our Beacon-1 models that beat 750+ competitors to win all three OpenADMET blind challenges. Whereas Beacon-1 provided a suite of models for individual molecular properties, Beacon-2 is a single system that predicts human dose end-to-end.
It has three configurable modules: an ADMET-PK foundation model, a potency module that can use our fine-tunable co-folding affinity model or physics-based methods like FEP, and a PK/PD model with physiologically-informed mechanistic models of pharmacokinetics.
We share validation results from Beacon-2 in a blog we are releasing today. We show that across 20 real-world drug programs running through Inductive, dose predictions from Beacon-2 agree with those from experimental data with a Spearman’s ρ of 0.61. And in a similar study on 325 publicly available compounds from the ExpansionRx OpenADMET competition, we show a Spearman ρ of 0.79.
As a proof-of-concept, we gave Beacon-2 to Indy, our medicinal chemistry agent, which used it over 5 autonomous design cycles on a recently disclosed SARS-CoV-2 program to cut predicted human dose 17x, which can be the difference between a drug program that gets killed and that makes it to clinical trials.
We compared the compounds that Indy designed using Beacon-2 against compounds from an optimization loop that used a potency-only reward function. The Beacon-2 compounds were favorable across three independent measures: they were preferred by chemists in blinded comparisons 87% of the time; were more druglike according to QED; and had a lower projected dose when computed by alternate methods relying on in vivo rat models and allometric scaling. Based on these encouraging results, we are now running prospective studies where compounds optimized by Beacon-2 will be validated in the lab.
Read the full post here: inductive.bio/news/beacon-2
At Inductive, our long-term goal is to build superhuman chemical intelligence, and by completing the reward function for autonomous drug discovery, Beacon-2 is an exciting step toward that goal.
Reach out if you’d like to get involved.
rohan ganesh retweeted
In our benchmarks our chemistry agent consistently outperforms generalist agents. Today we’re sharing some of these metrics (twice as accurate as Opus 5/Sol at finding data QC issues and better precision/recall at compound enumeration) and making the agent available to customers!
Today we’re launching Indy, @inductive_bio's medicinal chemistry assistant.
Originally built for our internal team, Indy learned from some of the industry’s best drug hunters how to best support real programs and is now doubling our team's’ capacity:
inductive.bio/blog/meet-indy
rohan ganesh retweeted
How aggressive a cancer is and how large is the benefit from chemotherapy are different estimands. Genomic assays were built decades ago for the first and repurposed for the second.
Today, we released 3 preprints on estimating the benefit of escalated treatment directly.
rohan ganesh retweeted
We've recently developed new causal AI methods that allow us to simulate patient outcomes under counterfactual treatment scenarios
Importantly, as we start to scale these world models, they begin to generalize zero-shot to multiple solid tumors - incredible work by our team:
Today, Ataraxis is announcing a new generation of AI models that learn to predict treatment response and patient outcomes, and generalize zero-shot from breast cancer to multiple solid tumors.
Blog post: ataraxis.ai/causal-ai-chemot…
rohan ganesh retweeted
Today, Ataraxis is announcing a new generation of AI models that learn to predict treatment response and patient outcomes, and generalize zero-shot from breast cancer to multiple solid tumors.
Blog post: ataraxis.ai/causal-ai-chemot…
rohan ganesh retweeted
We're putting our new coworker to work. Still needs some more training (and a name).
rohan ganesh retweeted
Turns out genome language models can design cancer vaccines.
Pretty exciting direction, the context-adapting machinery of language models is what may unlock personalized medicine.
Great work team!
@RadicalNumerics
1/5 We pretrained Omnii, a multimodal language model on DNA. Then, we asked it to design a cancer vaccine
In new research, we show how Omnii learned to turn a tumor DNA sequence into a personalized mRNA vaccine design.
Read more here: radicalnumerics.ai/blog/omni…






















