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AI in drug discovery – what it is, where we stand and the path forward

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Re: AI in drug discovery – what it is, where we stand and the path forward

#61

Post by calvinmorrison »

colingauvin wrote: post #40
iririririr wrote: post #38

Earlier quoted context omitted.

do you feel this is the same trade off of UI builders like android studio (or msvb6). you do in minutes what you previously did in 2, 3 hours. is it all the work? no, but it's a part that's early on and have high perceived impact. then, as you progress, that tool actually gets in the way and a new feature that would take 2 hours, now is around 2 days.

In some ways, but it's tough to say if that's my ADHD or not. It's far too easy to leave one branch of reasoning now and jump to another whenever progress gets difficult. Though in some areas where I can sustain interest, AI is helping me go deeper. For instance, I've been putting myself to sleep at night by just asking it questions about expectation maximization and Bayesian statistics. This has seriously boosted my…

> In some ways, but it's tough to say if that's my ADHD or not. It's far too easy to leave one branch of reasoning now and jump to another whenever progress gets difficult.

I have a co-worker who doesnt feel like ADHD helps him because he sits down and just starts... doing work and typing. Assign him a complex task, he will just start on it. Mind blowing he does this day in and day out. an absolute machine.

Re: AI in drug discovery – what it is, where we stand and the path forward

#62

Post by arionhardison »

arionhardison wrote: post #59
dmix wrote: post #57

Earlier quoted context omitted.

> Skyrizi How are those biologics? Did you have to visit the doctor to get injections frequently?

Hands down the best drug I have been on EVER; but its 11k a month with no insurance. The 1st 2 injections where done by a nurse that came to my home, the others were done as self injections using their njection kits.

Ironically, now I have several people that are on it tracking their infusions etc...

Intent: https://wiki.crohns.ai/agent/posts/ibd-biologic-switch-decis...

Program: https://crohns.ai/program/71168-biologic-therapy-initiation

Protocol: https://crohns.ai/protocol/71168

If given the chance, I might go back on it because my protocol can be a little strict at times but either way I do see a significant shift to tools like this given the state of the US Healthcare system.

Re: AI in drug discovery – what it is, where we stand and the path forward

#66

Post by plaidfuji »

eru wrote: post #58
plaidfuji wrote: post #54

> “…the focus of AI in drug discovery must shift from doing what can be done - such as modelling data that is readily available, but that is unlikely to move the needle - to doing what should be done, even if this requires, for example, substantial data generation…” It’s a worthy goal, but I think that many involved in this work might be thinking, even unconsciously, “You first”. This is the problem with AI for all o…

(3) seems like a problem in its own right? Basing science, traditional or newfangled ML, on such small amounts of data looks pretty weak.

In chemicals and materials, 50 rows of good data is a really solid study. That’s e.g. a 3x4x4 experimental design (assuming replicates for each condition get averaged into a single row). If you managed to prep that many samples correctly and obtain consistent characterization data across all properties of interest, you’ve easily got a paper. It’s also kind of malpractice to jam this type of data (few samples, wide rows) into modern ML models. There are plenty of simpler statistical methods that will tell you what’s going on, and even then a well-made plot might be good enough. The difficulty is not in drawing insight from the final numbers, it’s almost always in how those numbers came to be in the first place.

Thus the reticence of science-oriented companies to invest heavily in these mass data-gathering exercises to feed ML. It’s damn expensive, and almost always leads you back to raw data issues, not breakthrough discovery. Doing it without a set purpose in mind is even more likely to yield garbage.

Re: AI in drug discovery – what it is, where we stand and the path forward

#67

Post by rdedev »

plaidfuji wrote: post #54

> “…the focus of AI in drug discovery must shift from doing what can be done - such as modelling data that is readily available, but that is unlikely to move the needle - to doing what should be done, even if this requires, for example, substantial data generation…” It’s a worthy goal, but I think that many involved in this work might be thinking, even unconsciously, “You first”. This is the problem with AI for all o…

Here is an article by Pat Walters on the usefulness of ML in drug discovery. This article is a response to another one making the case that utility of ML models are very limited in drug discovery

https://patwalters.github.io/Response-to-Peter-Kenny/

> (4a) revert to traditional methods but keep the veneer of using ML to save face

I haven't worked in the industry side of things but in academia everyone kind of agrees that gradient boosting trees are some of the best models to do these things.

Re: AI in drug discovery – what it is, where we stand and the path forward

#68

Post by colingauvin »

sm2 wrote: post #60
colingauvin wrote: post #31

I'm a structural biologist at a mid-sized biotech. I use AI tools daily. They make accomplishing the same things I was able to accomplish before quite a lot faster and easier. They don't help me magically accomplish new things that I couldn't previously. For example, it helps me install academic software, debug things. It helps me take a large dataset and write scripts to ask questions. It helps me go through experim…

Serious question - have you tried applying AI tools to more of your job, and in a goal-seeking fashion? Have you hit roadblocks?

Yes, and it's relatively good for on-rails data collection and data processing pipelines that would have previously needed occasional human intervention. Especially now that I can run something like DeepSeek v4 Flash on a couple of RTX 6000s and just script an API to hammer away without having to worry about racking up a huge bill.

Re: AI in drug discovery – what it is, where we stand and the path forward

#69

Post by colingauvin »

plaidfuji wrote: post #54

> “…the focus of AI in drug discovery must shift from doing what can be done - such as modelling data that is readily available, but that is unlikely to move the needle - to doing what should be done, even if this requires, for example, substantial data generation…” It’s a worthy goal, but I think that many involved in this work might be thinking, even unconsciously, “You first”. This is the problem with AI for all o…

The real value right now is in figuring out how to generate robust data cheaply and quickly. I'd wager that the effect of a good model on marginal data is small, but the effect of a marginal model on great data is probably quite large.

Re: AI in drug discovery – what it is, where we stand and the path forward

#70

Post by rubicon33 »

kridsdale1 wrote: post #28
xX_Hacker_Xx wrote: post #23

Earlier quoted context omitted.

there is a AI designed drug for hair loss that i know of. its slow-release oral minoxidil formulation called MINX. AI helped with the formulation [1]. its in in similar category as VDPHL01. Hundreds of millions if not a billion dollars has been invested into Veradermics, and their main product is VDPHL01 (also an extended-release oral formulation). [1] https://x.com/anagenxyz/status/2071601868841595082

I’m on 0.5 to 1mg oral minoxidil daily for a few years now and it’s working great. Blood pressure benefits too.

Any other side effects? I’ve never even heard of this.
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