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Martin Borch Jensen

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@MartinBJensen

Longevity Industrialized.
@GordianBio: pooled in vivo screening for therapeutic abundance.
@NornGroup, @impetusgrants: 501c3 addressing bottlenecks to progress.
www.martinborchjensen.com open.substack.com/pub/norngroup Joined July 2014
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Martin Borch Jensen Verified account @MartinBJensen
Starting companies is hard, and it's a boon when you can learn from how others have done it.
There's way more content on tech than biotech, but I thought I'd put together an overview of the good resources that do exist:

For the big picture, start with @Atelfo blog post ✍️:
atelfo.github.io/2023/12/23/bio…
And if you want a fun read that tracks 'finding medicines' from prehistory to today, read The Drug Hunters 📚:
amazon.com/Drug-Hunters-I…

Now you know the system that exists. If you're a biologist, next step is to understand that finding a gene/protein target is just the beginning. 'What to do' is essential, but often things fail because you're able to do it in the right place for the right amount and duration (or even know what those are).

Here I'd honestly ask your fave LLM to walk you through 'Medicinal Chemistry 101' 🤖, covering potency, selectivity, the main modalities ADME, PK/PD, formulation, tox studies, CMC.
Make sure you understand the big differences between small molecules, antibodies, protein drugs, siRNAs, and cell/gene therapy.

After that, I'd work through a couple of concrete examples to see things in (messy) practice:
📚 “The Billion-Dollar Molecule” (and the sequel “The Antidote”) on how Vertex was formed and struggled their way to a first success.
📚 For Blood and Money by @nathanvardi. Same: fun & teaches you a lot about the confluence of science, money, and people that go into biotech.

After that, I'd go peruse @LifeSciVC's blog to understand how traditional biotech investors look at these endeavors - the main thing to pay attention to is what risks he keeps calling out.
Note that Atlas and other 'Boston biotech VCs' are a dominant group, but not the only way companies get funded. The original biotechs (all the ones called 'Gen' something), and the 2010s 'techbio' companies have a different thesis inspired by full-time founders as the scarce resource, though most of the risks are the same.
This post is a good place to start:
lifescivc.com/2025/10/twenty…

Finally, Derek Lowe’s “In the Pipeline” blog offers >20y of 1) commentary on biotech next, which you can now assess with the benefit of hindsight, and 2) more details on medicinal chemistry if you found that part exciting. Here's one starting place:
science.org/content/blog-p…
Speaking of, someone should take his best posts and turn them into a book... 😉

I'll expand each of the topics here into more resources throughout the week, stay tuned.
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Martin Borch Jensen Verified account @MartinBJensen
For no particular reason I feel compelled to RT the fact that curing diseases is data rather than intelligence limited.

For anyone serious about having AI accelerate cures, please get in touch so @impetusgrants can get you the necessary data.
Martin Borch Jensen Verified account @MartinBJensen Oct 26
Re. compute vs bio data collection, path is obvious when you consider EV of marginal spend on either:

If a frontier lab put $50M into collecting data that may be gating, e.g. rich multiomics from real human longitudinal trials, that's ~1 day of compute budget.
Such data takes incompressible time from study initiation.

If pure simulation doesn't need this data, cures delayed 1 day.
If the data guides models to simulate sooner, cures accelerated by months or years.
If the data is strictly necessary, cures accelerated years.
Obvious choice if eg the first AI benefit example in your S-1 is ""profoundly accelerate the rate of progress in biology and medicine, potentially curing diseases that have plagued humanity for millennia ..."
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Martin Borch Jensen Verified account @MartinBJensen
Get clustering boys!
Swann Bessa Verified account @swann_bessa 1d
Introducing e-means, an algorithm beating k-means in 10x fewer epochs.

With order of magnitude wall-clock speedups, e-means is released in FAISS and needs no hyperparameter tuning beyond k. It is robust to various scales and datasets. Still skeptical ? Just try e-means [1/10]🧵
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Martin Borch Jensen Verified account @MartinBJensen
JFC don't fuck this up already! Pass a f**** bill that create abundance, it's sitting right in front of you! You are doing the thing that made people vote for Trump, come on!
Nico Portuondo @NicoPortuondo 6d
SCOOP: Jeffries spokesperson says Senate permitting bill “falls short,” calls for more House input before they can support it:

"[It] falls short in several areas, including the manner in which NEPA reform is undertaken and the failure to adequately protect tribal sovereignty.
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