ChihChieh_Yu

Scientist & builder exploring experimental biology × AI.
Building to connect experiments, scientific agents, and collective scientific knowledge.

[ChihChieh_Yu @yu_chihchieh]

@tonychang430 @databricks @hanghuang_ Congrats!!

[ChihChieh_Yu @yu_chihchieh]

@akshay_manglik Just tried Pomerado and it’s really impressive. It’s so much faster than I expected!

One quick question: I noticed that the integrations I create seem to be public by default. Is that correct?

[ChihChieh_Yu @yu_chihchieh]

It’s really exciting to see what AI can do for biology.

But having spent years in the lab, I feel like even the basics of running and documenting experiments still need a lot of work.

AI is moving incredibly fast, but the experimental side needs just as much attention.

[Citrini @citrini]

There’s a really pronounced divide between people who are on the AI side of things and people who are on the biology side of things. Even among people working in AI, the divide persists based on where they’re focused.

The AI side, adherents of scaling laws and the bitter lesson, believe that AI getting better means AI being more capable of novel discoveries in medicine (that go beyond discovering new drugs in well understood targets or speeding up dry lab work etc).

The biology people think the idea of a bitter lesson in biology is kind of ridiculous and that regardless of AI’s capabilities it’s going to be at least 5-10 years until you could hope to reliably simulate human toxicology in silico (right now AI/ML can’t even reliably simulate in vivo studies like binding affinities or functional assays).

Even the most AI pilled of them, who will list all the areas that AI can reduce the time spent on various areas, don’t think the concept of AI discovering novel targets or generating new understanding of causal biology is realistic in the near term. Most, in fact, will readily characterize the assumption that even an “ASI” level model could find cures hidden in our current understanding (without expanding that understanding with significantly more biological data) as patently false.

This was a big part of why we wanted to experience it ourselves by using AI for a (very) small portion of what it can do in biology. It was both mind blowing and revealed some poignant insights into how different the problem of making AI better at something like coding is to the problems of biology.

Even if it unlocked the ability to generate new molecules at a stunning pace, its simulation (in silico) of binding only had 49% accuracy. And if we wanted to improve that, we’d need to feed it back the data from the lab - data that took about 30x as long to get as it took to generate the molecules. And these were tests in a test tube (turn of phrase), imagine how long it would take if we wanted to improve the model with data that required recruiting humans and monitoring their reactions.

Human biology is very complex, and adding a drug - especially a novel drug and doubly so for a novel drug acting on a novel mechanism - means mapping interactions that have nth-order-effects that we don’t have the data to predict. Virtual cells have made advancements enabled by AI, but the perturbations equate to a small fraction of the possible.

This is before you even get into curing disease, especially uniquely human diseases. Brain function involves cellular identities that are unique to humans, meaning animal studies are of little use.

How long does it take to understand what the side effects of chronic administration of a drug in humans are? AI can speed up analyzing the data, or designing the trial or even getting it approved…but it can’t make a 6 month trial complete in 3 months.

I think its important to point out that significant advancements can be made in AI that contribute to biology, while also being realistic about how much it speeds up something as complex as curing a specific cancer or diseases of the brain.

[Citrini @citrini]

There’s a really pronounced divide between people who are on the AI side of things and people who are on the biology side of things. Even among people working in AI, the divide persists based on where they’re focused.

The AI side, adherents of scaling laws and the bitter lesson, believe that AI getting better means AI being more capable of novel discoveries in medicine (that go beyond discovering new drugs in well understood targets or speeding up dry lab work etc).

The biology people think the idea of a bitter lesson in biology is kind of ridiculous and that regardless of AI’s capabilities it’s going to be at least 5-10 years until you could hope to reliably simulate human toxicology in silico (right now AI/ML can’t even reliably simulate in vivo studies like binding affinities or functional assays).

Even the most AI pilled of them, who will list all the areas that AI can reduce the time spent on various areas, don’t think the concept of AI discovering novel targets or generating new understanding of causal biology is realistic in the near term. Most, in fact, will readily characterize the assumption that even an “ASI” level model could find cures hidden in our current understanding (without expanding that understanding with significantly more biological data) as patently false.

This was a big part of why we wanted to experience it ourselves by using AI for a (very) small portion of what it can do in biology. It was both mind blowing and revealed some poignant insights into how different the problem of making AI better at something like coding is to the problems of biology.

Even if it unlocked the ability to generate new molecules at a stunning pace, its simulation (in silico) of binding only had 49% accuracy. And if we wanted to improve that, we’d need to feed it back the data from the lab - data that took about 30x as long to get as it took to generate the molecules. And these were tests in a test tube (turn of phrase), imagine how long it would take if we wanted to improve the model with data that required recruiting humans and monitoring their reactions.

Human biology is very complex, and adding a drug - especially a novel drug and doubly so for a novel drug acting on a novel mechanism - means mapping interactions that have nth-order-effects that we don’t have the data to predict. Virtual cells have made advancements enabled by AI, but the perturbations equate to a small fraction of the possible.

This is before you even get into curing disease, especially uniquely human diseases. Brain function involves cellular identities that are unique to humans, meaning animal studies are of little use.

How long does it take to understand what the side effects of chronic administration of a drug in humans are? AI can speed up analyzing the data, or designing the trial or even getting it approved…but it can’t make a 6 month trial complete in 3 months.

I think its important to point out that significant advancements can be made in AI that contribute to biology, while also being realistic about how much it speeds up something as complex as curing a specific cancer or diseases of the brain.

[Julian Englert @julian_englert]

We built a website to make it super easy to get started with protein design and participate in the Anthropic x Adaptyv protein design competition

▶️ https://t.co/L3dg6H6BTU

This week, we're challenging people to design an Ozempic-like drug. You can get started in just a few clicks, using the newly released RFD4 model to design proteins targeting the GLP-1 receptor.

We'll synthesize and test 1000+ of these designs in our automated biology lab, including whether they actually activate the cell receptor, and make all the results publicly available

[ChihChieh_Yu @yu_chihchieh]

@FishAudio DRAMA

[ChihChieh_Yu @yu_chihchieh]

@tonychang430 I'd love to try it out!!!

[ChihChieh_Yu @yu_chihchieh]

I just reserved my Cloudflare Wallet tag: https://t.co/OwkDAEsd8s.

Reserve yours now at https://t.co/nihT03WigW

[Maor Shlomo @MaorShlomo]

TLDR: I’ve started a small fund from my Base44 money that’s focused on cancer research and breakthrough therapies.

I’ve invested in 7 incredible companies in the past 4 months and plan to do more as my liquidity allows. Listing my investment thesis below

I welcome anyone like me who has been blessed financially to join me and find ways to finally manage or even end this disease.

Longer -

I honestly believe cancer is humanity's worst enemy.

~40% will be diagnosed with it, and out of which a ~third won’t make it. And numbers are currently not getting dramatically better.

For many it might sound like a statistic
But for some of us who have been there ourselves or had to go through it with a person we love, it’s one of the worst things you can imagine.

I had to go through it with my mom - who passed away 4 months ago and whom I miss and think about every single day since.

This field needs a dramatic push.
The current wave of technology and AI is doing a lot of good in the world,
But cancer therapy is one of those things that are not moving nearly fast enough.

And there’s no good reason for that - AI is creating so much opportunity right now.

Here's my thesis on how things will play out:

For drug development, the bottleneck will shift to
1. Manufacturing
2. Real world testing

"In silico" is going to accelerate incredibly fast. AI is identifying new target proteins and designing new molecules, drastically shortening timelines.
AI will create an abundance / inflation of ideas. It will help design molecules faster than anything historically possible.
This is already happening.
Play with Claude Science or Codex and you'll be surprised at how far you can get with just a few prompts.

We need a better way to *manufacture and test* the 1000s of ideas coming from AI models spitting out potential drug candidates

1. Manufacturing: i.e. how fast can we synthesize a potential drug

Not too far into the future, companies with manufacturing excellence will win over companies with the smartest scientists.

Being able to synthesize a drug candidate fast - together with efficient methods to test efficacy (more on that below), basically means you get more shots at a target hence more chances at succeeding.

This is the same exact lesson the software industry learned about the importance of fast iterations.
Companies like Starget Pharma are leveraging AI heavily to iterate on drug candidates + figured out in-house manufacturing in a way that lets them iterate on drugs almost as fast as if they were a software company iterating on features.

2. Real world testing: i.e. how fast can we get a call on a drug's efficacy.

We need better real-world indicators for drug efficacy than mouse / animal models.
The success rate of animal models -> human clinical trials is less than 8%.

Breaking down this bottleneck will change the entire industry.
Developing an oncology drug and taking it through all clinical trials usually cost >> $ 100m
Thus taking the wrong bet is devastating (but still, happens a lot).

Giving drug development companies the ability to better understand the efficacy and mechanisms of their drugs will change a lot. Esp in the age of abundant drug candidates.

Furthermore - and once those methods proved efficient - Joining forces with regulators to shorten (or even skip) some clinical trials altogether will cut years from the process and save many lives by bringing new drugs to market faster.

CuResponse is pioneering functional testing - taking real cancer biopsies and showing drug efficacy with very high precision. Can be used for precision oncology as well as testing new drugs.

Cellint is providing a fully automated cell-culture R&D platform for companies and researchers.

----

I’d love to see a future where there are “cloud services” for drug development companies.

A company should eventually be able to submit a drug candidate programmatically and receive:
1. The synthesized drug.
2. Testing across cells, tissues, organoids, and tumoroids.
3. Structured results showing what worked, what failed, and why.
4. A recommended next iteration derived from the biological reactions.

I’ve invested in all of the above companies and am searching for additional companies to bring this “cloud services for drug dev” vision to life.

----

Alongside it, I am also investing in direct therapeutic moonshots.

I invested in Baccine, which is developing a bacteria-based cancer immunotherapy platform and deserves an entire separate post.

I’m also a proud LP in Even One Ventures - @sytses ’s fund to fight cancer. Sid and Jacob Stern are an inspiration to me as I’m making my first steps in the field, and I bet their companies will make a massive impact.

And 3 other incredible companies focused on how therapies are delivered - where I’ll elaborate in future posts.

----

I’m still obviously spending most of my time on Base44. Which is a plus for the companies - as we’re able to support them with tools, software, AI agents, etc.

I’m looking for people to join me - invest side by side or help one of the companies.

I don’t have a name for the fund yet, nor an email or a website
I will get to it this weekend and will post it in the comments.

[Oliver Hahn @Oliver__Hahn]

Can you rejuvenate an old brain by giving it young immune cells? 🧠
My lab @calico put it to the test. In our new study, we replaced the brain's immune cells in old mice with young ones.
The result? The old brain environment forced the young cells to age RAPIDLY. A 🧵👇

[Martin Picard @MitoPsychoBio]

Some unhealthy mitochondria develop rigidified cristae that bend at very specific angles

[Niko McCarty. @NikoMcCarty]

I made an interactive enzyme simulator.

Change the temperature, add or remove inhibitors, and tune the probability that enzymes bind their substrates. Watch as the reaction rate moves up or down in real-time.

Maybe this will be a helpful teaching tool.

[Santosh Chauhan @Chauhanlab_ILS]

FROM CELL DEATH TO REGENERATION: What if dying cells -come back to life? Now online @embojournal I https://t.co/eOKTg6qVnk , we aim to answer this question. Here is the journey. If you🩷science, please dont 🛑till the end of 🧵, I promise you will not regret. 1/n

[Ruben QC @RubenQC_Lab]

⚠️📢 Happy to share our latest review with @LabScorrano, now out @MolecularCell. Get to know how mitochondrial fusion and fission is orchestrated to define mitochondrial shape and function! @InstitutoCajal
@csic @fund_tatiana https://t.co/I2GxelIPvE

[ChihChieh_Yu @yu_chihchieh]

Encoding of hunger by the neuronal epigenome slows aging in Drosophila https://t.co/OURUN1Fyj1

[Bloomberg @business]

Health experts say intermittent fasting, keto, and avoiding extra sugar are dietary changes worth considering https://t.co/mRvaJmBWoq via @BW

[Prof. Feynman @ProfFeynman]

Life is too short to worry about stupid things. Have fun. Fall in love. Regret nothing, and don't let people bring you down. Study, think, create, and grow. Teach yourself and teach others. 🧠

[Nature Reviews Endocrinology @NatureRevEndo]

Our 6th most popular Review from 2021 is 'Lipid and glucose metabolism in white adipocytes: pathways, dysfunction and therapeutics', by @DominiqueLangin and colleagues (£)
https://t.co/us51tk4cCY

[Cathrine Sant @cathrine_sant]

When you tell an 'omics lab to dress up for Halloween.. @doctorcorces