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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

#71

Post by [deleted] »

rdedev wrote: post #67
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…

[deleted]

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

#72

Post by hibikir »

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.

I worked on some of the very best funded plant research out there. When it comes down to it, there's enough variation caused by confounding factors, and it takes so long to capture more data, that almost everything anyone tries cannot be called a success or a failure for years, because the individual measurements for one small plot of land somewhere just don't mean anything. Once you do an entire experiment for a season, which takes months, and you grab the little noisy data you have, and turn it into real rows, we were down to very little.

You can do more tests on smaller things, like checking if some protein will kill some cells of a pest, but making sure a plant produces it enough that it actually does something significant to the real, live pests, that it's not toxic, and it doesn't harm the plant's yield massively (as it's now spending time producing your pesticide) is still going to take years. We might be able to fold proteins, but the kind of things we'd need to really simulate plant biology well enough to not need years of failures are still very far away.

And it's far worse in medicine, as with plants at least nobody has ethical concerns if they fail and die, and nobody needs to get consent from a corn seed. Getting to 50 actual data points from many medical studies is already a lot of effort. And imagine when it's a long term study, and you need to follow patients for 30 years, as theym move, or die, or decide to stop participating, or who knows what.

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

#73

Post by [deleted] »

pton_xd wrote: post #16
skepticATX wrote: post #12

Earlier quoted context omitted.

About 2% of finasteride users experience these side effects, and they are reversible after discontinuation.

Apparently some users report persistent side effects even years after stopping the medication (post-finasteride syndrome).

[deleted]

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

#74

Post by mjmj »

rubicon33 wrote: post #70
kridsdale1 wrote: post #28

Earlier quoted context omitted.

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.

I’ve taken low dose oral min for a few years and it’s systemic, meaning it’ll make all your hair grow. I now have body hair where before it was never noticeable. “I’m hairy like monkey” as my kids say.

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

#75

Post by bharatsuthar »

techpression wrote: post #56
Oarch wrote: post #20

[AI drug discovery] was never the hard part.

This made me laugh, more than expected, but I did visit LinkedIn just before so that could explain it. Thanks!

Yeah it's [x was never the hard part] all the way down. It's a very human thing and I bet we'd keep saying that even after we've solved the hardest mysteries including consciousness, the origin of life or the true nature of reality.

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

#76

Post by bharatsuthar »

immmmmm wrote: post #26
KellyCriterion wrote: post #22

Earlier quoted context omitted.

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

Psychedelics are several orders of magnitude stronger on the plasticity front. In therapy as well.

So is ketamine but we haven't explored it more for other brain functions where it can act as a psychoplastogen eg reopening critical periods of visual learning.

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

#77

Post by aragilar »

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 obvious question is what limits getting more data? Astronomy (especially in Australia) has been quite good at designing surveys to answer multiple scientific questions with reasonable amounts of data (and then fed into ML systems like the cannon). Sadly one of the consequences of the LLM hype is the increasing cost of doing this, so "AI" is actually making things worse not better.

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

#78

Post by bharatsuthar »

consensus1 wrote: post #29
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.

Psilocybin has this effect. Source: I can't remember where I read it, so low confidence.

Sure, but what kind of protocol are you going to use to actually reap the benefits of these transient neuroplasticity effects, especially for learning something new like math or piano as an adult?

Same with ketamine. It's not like you can take it a couple of times and open some magic window where everything suddenly becomes easier to learn. From my personal experience and surface-level understanding of the current research, psychedelics(including ketamine) seem to temporarily relax hardened beliefs/priors and rigid neural pathways, which can help you see things from a fresh perspective. But learning something substantial like math or a new language after you've passed the most plastic stages of development is still going to take much longer than what these drugs can realistically help with.

They might make you see something differently or even get you extremely interested in it, but sustaining that interest and consolidating the knowledge or skill is still slow compared with childhood/adolescence. Of course, it depends a lot on what you're learning. For example, crystallized intelligence and sufficient motivation can make some things much easier to learn as an adult, but many useful things are also just boring to learn when you no longer have a childlike plastic brain or an environment built around constant learning.

Edit: Theoretically, you could accelerate learning by taking psychedelics/ketamine at a set frequency but it's a huge gamble because of their risk profile. eg. HPPD/trauma risk with classic psychedelics and bladder/neurotoxicity risk with ketamine if you get addicted or take it too frequently.

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

#79

Post by bharatsuthar »

hibikir wrote: post #72
eru wrote: post #58

Earlier quoted context omitted.

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

I worked on some of the very best funded plant research out there. When it comes down to it, there's enough variation caused by confounding factors, and it takes so long to capture more data, that almost everything anyone tries cannot be called a success or a failure for years, because the individual measurements for one small plot of land somewhere just don't mean anything. Once you do an entire experiment for a sea…

Ethical concerns might become an even bigger bottleneck in the future. It's sad what we're doing with millions of rodents each year.

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

#80

Post by murphyslab »

bigDinosaur wrote: post #49
bogzz wrote: post #45

Earlier quoted context omitted.

Isn't the comment in fact praising Derek Lowe as a science communicator? In the second paragraph OP is just posing the questions that one might have when reading about a field not your own, that highlight the importance of reliable science communicators.

It's a very confusing comment since Derek Lowe is a chemist working in pharma but is being referred to only as a science communicator which I suspect most people would consider to be an implicit insult.

Derek Lowe is someone whom I greatly admire and his work as a science communicator is a true service to the profession and to science at large. It is not intended as an insult in any way. I've known a great number of competent chemists, but few are as effective as communicators as he is.
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