DIMENSION

Dimension is a multistage, research oriented, investment firm partnering with companies across the frontiers of science and compute

[DIMENSION @_DimensionCap]

Reflecting on 2024: a category sweep of the Nobels, AI enabled clinical readouts, and the first trillion dollar pharma in sight.

We stand on the shoulders of giants. Looking to 2025: This is just the beginning. Make no small plans.

[Alex Wolf @falexwolf]

We’re thrilled to share that Lamin is now generally available! With just a few clicks, anyone can create a database on https://t.co/3zIxWeO17Q

In 2022, we set out to build an open-source data management tool that’d make it easier to scale AI workflows in the life sciences. Over the past few years, we’ve served research labs and enterprises of all sizes with what used to take a dozen tools:

- Data lineage
- Format-agnostic lakehouse
- Notes, ELN, LIMS, ontologies
- Versioning, branching
- Data lake, catalog
- Agents, workflows, notebooks
- Feature store, MLOps

With Lamin, it’s just Postgres and storage in a Python package, plus an optional GitHub-like web platform.

Give it a try: pip install lamindb

Thanks to @_DimensionCap and @ycombinator and many others who made this possible

[Jacob Kimmel @jacobkimmel]

summer @newlimit: we found our best age reprogramming payloads yet, hit more drug dev milestones than ever before, & welcomed new leaders to the team.

it's the most exciting time in our history & we hope you consider joining us.

highlights

- >2X increase in regenerative restoration in old hepatocytes
- +8 payloads restore endothelial function, +5 in hepatocytes
- >300X scale up of therapeutic manufacturing, 13X quality improvement
- 5X increase in sample efficiency for reprogramming AI

[Nan Li @nanli]

Excellent read covering the underlying theme of surface receptor mapping that has been behind recent Pharma success stories.

Outside of the tremendous pace of progress in AI, scaled molecular interrogation and experimental precision continue to play a massive role in scientific progress in biotech. Written by the one and only @ChemVagabond!

[Nikhil Namburi @nikhilvnamburi]

1 year ago, i profiled my agent workloads (hosted on @modal). it corroborated my hunch that a $100b CPU startup could be built off SOTA single core perf

we’ve since backed the best CPU core architect in the world. more soon ⏳

all you need for investment ideas is a @modal login

[Zavain Dar @zavaindar]

i can't stress enough how research team guides our investment team, and vice versa. for bread crumbs of where we've been thinking, building, & investing, i read @ChemVagabond's terrific analysis & future perspective of understanding, manipulating, and drugging the surfaceome

[Simon Barnett @SimonDBarnett]

We have a new essay out about how our growing understanding of cell membrane dynamics is leading to better medicines.

This work was led by my fantastic colleague Frank (@ChemVagabond), who commands the topic so well given his time at AbbVie and Moderna doing relevant work.

ESSAY LINK:
https://t.co/ET3WAHeFbo

~~~

I really enjoyed Frank's usage of HER2, perhaps the most well-known cancer target, to tell this story.

I was surprised to learn how the rolling advent of new measurement technologies keeps kicking up more therapeutic innovation. With each new way to study cell surface dynamics, there's a new little widget of the biology that drug developers can cleverly exploit to fix something about previous-generation drugs, or to make new drugs altogether.

During our behind-the-scenes conversations, I kept thinking of this Mark Twain quote:

"It ain’t what you don’t know that gets you into trouble. It’s what you know for sure that just ain’t so."

With each new tool, it's important to reconsider whether prior knowledge continues to get supported, or if it begins to erode.

When I analyze preclinical data or start to understand a therapeutic hypothesis built on years of evidence, it's good to continue to ask this question. For example, the shared prior might be, "this is a bad internalization target", when in reality, maybe it's actually a great internalization target, and the previous set of tools landed at the wrong conclusion because of this limitation.

Nonetheless, I enjoyed learning from Frank about these assay techniques and the backend approaches to integrating their signals in a clever way.

It's timely because new biology is incredibly important for building better medicines.

[Frank Gao @ChemVagabond]

Over four decades, HER2 went from a marker of poor prognosis to the target of many transformative medicines.

Can we make this story repeatable at scale with unprecedented speed?

[Chris Gibson @RecursiveChris]

There’s a lot of truth embedded in this hilarious comment.

Yet, at the end of the day, I think almost everyone would choose better medicines over trick cars (though kudos to @Tesla because that shit is cool)…

But our life science ecosystem is less comfortable with big swings for the fences than in tech, which creates a self-perpetuating cycle of risk-resistance.

Maybe it is because so many drugs fail… even the conservative bets are risk-on. But therein lies the point - all bets are risky in the old systems, so we need to find new ways to think and systems for discovery, not just new drugs.

Kudos to the pioneers out there trying to make the system better.

[Zavain Dar @zavaindar]

we first met @bernhardsson and @modal negotiating a compute contract for a neolab (achira) we were building internally at @_DimensionCap

erik left such an impression that we quickly became investors. it's since become (in a mix of primary and secondary) amongst the largest investments across our portfolios

valuations are a lagging indicator of genius, invention, and execution. congrats to Erik, @akshat_b and the entire Modal team

ad astra

[Simon Barnett @SimonDBarnett]

It is almost 2027 and I cannot believe we are still talking about AI drugs, this time revived by Shkreli's comments on the live All-In podcast.

There is literally no such thing as an AI drug. Never was.

So playing the game of tracking when one will be clinically approved, how many are created a year, what the cost-delta is between them versus 'regular drugs', all of these debates begin from a false premise and are thus not useful to talk about IMO.

Don't get me wrong. ML methods are extremely powerful and have already clearly and unequivocally contributed to many successful drugs. Like that's not even a debate.

Every now-genetically-validated target discovered by sequencing? Thank god we had CNNs and other older ML methods to convert the fluorophore data into useful signal.

Every small molecule developed using older ML-based classifiers during virtual screening. That's ML too.

Of course, these are the 'boring' ML methods that people don't even count anymore because they're way past their hype phase and have become silent infrastructure underpinning the whole industry.

In ten years, today's hype methods will do the same. They'll lose their sheen and people will goalpost move, or they won't even acknowledge them as AI/ML because they'll be so busy saying whatever current thing from 2035 is fake and will never contribute.

All sufficiently useful technology moves through these cycles. Once it works, it won't be called AI anymore. It will just be math. Or statistics. Or something else.

Any drug program from here on out will use some form of ML, hype or not, so arguing about some false binary whether a molecule/program is 51% AI is just ludicrous to me IMO.

This isn't even about Martin at all. Like I get his frustration, and I think it behooves anyone to be skeptical when magical elixers are passing hands.

It's just that I think this discussion has run its course and I don't think it's a good use of time.

[Joshua Meier @joshim5]

Drug discovery is where frontier AI can matter the most.

Today we're announcing @Chaidiscovery’s collaboration with @GSK, following a successful wet-lab evaluation of our models. Using Chai-3, @GSK found binders to every target tested.

AI is entering its proof-point phase.

[Zavain Dar @zavaindar]

“They published a paper, which was pretty impressive, and we of course didn’t believe it… I’ve learned to be skeptical on these things and this outdid my expectations” - Chris Austin, GSK SVP R&D technologies.

6th deal in the books for @chaidiscovery just this year. slowly. and then all at once.

https://t.co/HRsGTLyGIS

[Bauer LeSavage @bauer_lesavage]

Immune diseases are dynamic, and our immune systems change drastically as we age. Collecting high-quality, multi-omic, longitudinal data from patients is key to understanding how to best treat these disorders. Super excited to announce our partnership with @onyxbio (@rgrchen @amlakhan) to do exactly that!

[Amit Lakhani @amlakhan]

@rgrchen and I started @onyxbio to understand how real immune systems evolve in real people. By generating multiomic datasets from our members over time, we aim to help cross the trial failure chasm.

Shoutout to @_DimensionCap @haystackvc @refactor and more for believing in us!

[Roger Chen :: @rgrchen]

Excited to introduce Onyx and announce our $8M seed round led by Dimension to build a virtual immune system!

Onyx has always been a story of connection. The first connection was to my cofounder @amlakhan, a deeply curious technologist who I’ve been fortunate to call a friend for 19 years now. I've always said that working together was a cornerstone of starting Onyx, and it truly has been one of the best decisions I've ever made.

Connection is also at the heart of what Onyx does. We believe AI is key to making biological complexity tractable, ultimately transforming life sciences into an engineering discipline. But that requires the right data. AI performance compounds with context, and richer biological context comes from more connected data.

In biology, such context is often missing: molecular data, clinical history, and change over time are typically captured in isolated fragments. Onyx provides the substrate that connects them, pairing genomics, transcriptomics, proteomics, and metabolomics with rich clinical histories. By studying these layers together in people over time, our data platform traces how real human biology changes in response to infection, inflammation, and treatment. Seeing how these trajectories differ across people brings us closer to understanding what drives disease—and to developing precision medicines that work better for real people.

Yet connecting data is only possible because of the people who have connected with our mission. Nearly a thousand members have already joined our research studies. They are more than research participants: they are difference-makers transforming how we understand some of humanity’s most complex and chronic diseases. I always say that our work is for people, but the truth is that people make our work possible. That connection comes full circle, and at Onyx, people will always come first.

Finally, as we raised our oversubscribed round, we sought more than just business partners. We wanted investors we felt a true connection to—not merely sources of capital, but co-conspirators in creating meaningful change. To that end, we feel incredibly fortunate to have joined forces with @_DimensionCap, @haystackvc, @refactor, @standard_vc, @robotventures, and many more.

Now back to building!

[Nan Li @nanli]

Excited to announce our partnership with @rgrchen, @amlakhan and the team at @onyxbio!

We believe that precision immunology is one of the most interesting directions of new medicines and that multi-modal, longitudinal datasets from real patients are needed to power this field.

Onyx is building a unified data platform for immunology - combining clinical records, multi-omics, sample banking, and patient reported outcomes. This platform helps researchers to understand how immune diseases progress, why patients respond differently to treatment, and uncover new ideas for medicines. At the limit, these data will power the buildout of a entirely virtual immune model to simulate human immunology with high precision.

We have known Roger for over a decade and are excited to reunite! Nearly a thousand patients are already enrolled onto the platform and looking forward to much more to come!

https://t.co/CQX5eZeVMZ

[Zavain Dar @zavaindar]

Earendil 🤝 Genentech. Another one.

[Viswa Colluru @viswacolluru]

I started @enveda so mothers could fight their diagnoses with power mine never had. Sept 18 is her birthday. It’s also the day our first IND cleared, our first patient data came in, and now our Series E closed. More backup for the fight ahead. Happy Birthday, Mom!

[Enveda @enveda]

Today we are announcing a $311M Series E to bring pharma into the 21st century.

Enveda started with a thesis: a molecule that evolution has already refined is a better place to begin than one designed from scratch. This year, two first-in-class molecules discovered by Enveda (ENV-294 and ENV-308) had positive readouts in humans. Many more are behind them.

We are grateful to @CatalioCapital for leading this round, to our new investors Durable Capital Partners, @ICONIQCapital, @lightspeedvp, Surveyor Capital (a Citadel company), accounts advised by @TRowePrice, @digitalisvc and Alderline Group, and to @BaillieGifford, @PremjiInvest, @trueventures, @KinnevikAB, @FPVventures, @_DimensionCap, @lifeforcecap and @Lux_Capital for continuing to back us.

The best is yet to come…

Read more: https://t.co/6TKqdMpnS5

#Biotech #AI #LearningfromLife

[Simon Barnett @SimonDBarnett]

Molecular design tools are nowhere close to commoditization, have tremendous headroom left to improve, will generate substantial value for those who move the needle, and are clearly the bottleneck for a meaningful set of potentially high-value targets.