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

science.org

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

#31

Post by colingauvin »

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 experiment drafts to see if I'm missing things. It helps me remember obscure formulas I use every 6 months. It has not, at least in my experience, come up with anything truly novel.

A concrete example: AlphaFold is great...to come up with a starting model for a chimeric fusion or something. What would have taken me 1-2 hours fumbling around in PDB or CIF files is now a quick prompt.

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

#32

Post by largbae »

How refreshing was this article vs. all the slop?

The lack of comparable data and testability really does seem to be a challenge. I wonder if people would be more willing to collect and share lots of health data if the collecting company was a non-profit dedicated to anonymizing it.

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

#34

Post by murphyslab »

tim333 wrote: post #30

Derek Lowe discusion of the paper https://www.science.org/content/blog-post/so-how-ai-drug-dis... I think that was originally linked but got changed to the £30 to Elsevier version for some reason.

Derek Lowe as a science communicator, and others like him, is sorely needed to understand the real meaning and significance of the study and others. I say that as someone with a PhD in chemistry who's been to plenty of presentations on drug discovery topics.

It's difficult to calibrate statements made by other scientists unless you're well embedded within a field: Is this someone whose opinions matter? Are they the subject matter expert they make themselves out to be? Is this research itself truly impactful? Is it really 5 years until it will be realized outside of academic labs? Etc...

It's difficult to decipher questions around credibility because they rely on real-world interactions and associations that extend beyond the physical tokens of paper counts, publication venues, citations, and author lists that typically lag behind the front of human knowledge which is generated from real-world interactions. It can be simple things, like the insightful question a grad student, with minimal publication history, asks in a seminar.

Of course, the paywall is also unhelpful too, but a good, brief commentary by an appropriate commentator is a better link for 99% of prospective readers compared to most "peer reviewed" (scare quotes because that's a real question nowadays) articles.

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

#35

Post by WarOnPrivacy »

snapetom wrote: post #19
WarOnPrivacy wrote: post #18

nerd-fanboi proposal: Articles by national treasures (like Derek Lowe, Raymond Chen) should be highlighted with specific identifiers on HN - like a distinctive title font or an ascii diamond ◊.

No thanks. Social media needs less hero worship. Just RSS whoever you like.

I agree. I won't do heroes. Ever.

A National Treasure, on the other hand - they enrich life without being a vector for tribalism (eg:Michael Kramer/Kate Reading).

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

#36

Post by [deleted] »

KellyCriterion wrote: post #22
cryptographical wrote: post #21

need one for brain plasicity. it would be nice to be able to easily learn a foreign language or musical instrument naturally.

There is one! Ketamin should have huge impacts on neuro/brain plasticity when used properly (i.e. in therapy)

[deleted]

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

#38

Post by iririririr »

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…

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.

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

#39

Post by AnodicElegy »

tim333 wrote: post #30

Derek Lowe discusion of the paper https://www.science.org/content/blog-post/so-how-ai-drug-dis... I think that was originally linked but got changed to the £30 to Elsevier version for some reason.

OP here: the title of the thread still links to Derek Lowe's blog post, but the article discussed in the blog post was added to the body of the original post (not by me).

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

#40

Post by colingauvin »

iririririr wrote: post #38
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…

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 understanding of cryo-EM alignment algorithms in a way I couldn't do in grad school because there was no professor that understood enough to help me when I got stuck reading literature.

So it's a double edged sword for sure.

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