r/biotech • u/LegitimateBoot1395 • Oct 13 '24
Is AI in drug development built on sand? Open Discussion šļø
Since 2022, big tech has spent over 150 billion+ investing in infrastructure, in house AI models and acquiring AI startups, etc. OpenAI has raised $13 billion and is losing money on an unprecedented scale as it has yet to really come up with a use case that people will actually pay market prices for.
Despite this insanely large investment, the results so far are a few Large Language Models which continue to get things wrong and generally have not developed at the speed predicted..see the recent OpenAI launch of "strawberry" which most commentators say was pretty disappointing and in no way a step change.
Considering what AI drug development companies say they are doing, on a fraction of the budget, convince me that it is not the latest house of cards ready to start crumbling down after a few high profile trial failures.
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u/bozzy253 Oct 13 '24
Just my perspective, so take it with a grain of salt. Think of the MASSIVE amount of data that ChatGPT scraped, stole, scavenged, translated in order toā¦ make a half decent recipe or suggest a vacation. It still makes obvious errors.
Now think about a model needed for drug development that would actually propel us into the future. Think about how much perfect data that would take. Think about how many errors it would make that couldnāt be validated except through costly experimental data.
I believe AI has its place in biotech, but most of the current technology is probably proof of concept vaporware.
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u/adingo8urbaby Oct 13 '24
This is it. Most do not realize the incredible amount of shit data or at least the large amount of well curated data necessary to make this work. And where is all of the pharma data and even the academic data? Locked up in proprietary databases, in local excel spreadsheets, in paper lab notebooks, etc. It will take a momentous effort to take advantage of this data. And the ultimate problem may be that a p value less than 0.05 is just not stringent enough and we need to rethink our statistical analysis. This means that unless we are looking at the raw data itself, the published results may be all but useless (down with journals, up with database based publication!). More rigorous hypothesis development, data modeling, data storage, and statistical analysis will be required to take advantage of many of these systems.
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u/Reasonable_Move9518 Oct 14 '24 edited Oct 14 '24
AlphaFold built on decades of painstaking standardization of techniques and data formats for structural biology.
Ā ChatGPT et al. are built onā¦ the internet.
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u/The_Infinite_Cool Oct 14 '24
And with all that Alphafold still has limited ability to predict structures with limited homologous information. I don't need AF to predict antibody domains or explain known structures, I need it to take intrinsically disordered proteins and accurately predict druggable locations.
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u/errantv Oct 14 '24
The problem is that ML isn't creative....it's designed to use a huge set of training data to identify patterns it's seen before in a prompt it hasn't seen before. It's really, really great for things like identifying tumors in medical imaging data because there's a gigantic set of training data and pretty limited possibilities for encountering a pattern that is truly unique/original. It's very much NOT designed to identifying a NEW pattern or structure that it doesn't have an extensive training set to compare to.
So it's not going to reliably identify structures in disordered proteins because it doesn't have the training data to make the identification.
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u/shabusnelik Oct 14 '24
Depends, given enough data and the right training method/architecture a model can generalize beyond the training data distribution, see robots trained on simulated data that work in real life.
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u/WeTheAwesome Oct 13 '24
Itās definitely a hard problem to solve but itās not an unsolvable problem. Check out federated learning which is designed to work with siloed proprietary data. Itās still a burgeoning field and there are still business/ legal aspects to hash out but progress is already being made to address this challenge.Ā
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u/Auzzie_almighty Oct 13 '24
I sort of disagree, Iāve used the tools coming out of David Bakerās lab to design proteins and they work amazingly well, all things considered. I managed to design a fully functional RNA binding domain, between proteinMPNN and some rational design. It needed lot of development, and since it was in a small startup on its last legs at the beginning of 2023, that couldnāt happen but itās amazing it worked at all.
I view the current technology as magic, but like the old dark European fairytale kind that requires a ridiculous amount of esoteric knowledge or dumb luck to function right
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u/thisaccountwillwork Oct 13 '24
Protein design isn't really remotely close to the complexity of modeling drug responses in humans though. It's an apples to oranges comparison.
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u/Auzzie_almighty Oct 13 '24
It was protein design for gene therapy so it was intended to be a therapeutic same as any drug and Iād put initial design as a part of drug development. AI isnāt useful in any downstream areas of drug development yet but the discovery phases and preliminary research, itās doing absolute wonders
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u/thisaccountwillwork Oct 14 '24
Design is surely a part of drug development but that is not what I wrote.
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u/Bubbyjohn Oct 14 '24
Honestly, protein design in discovery is probably the hottest new AI question. Imo, small molecule drug development in human response is secondary to the more personalized medicine approach that gene therapy can offer
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u/thisaccountwillwork Oct 14 '24
It's not a question. It's a fact, but that is not why there is so much buzz around foundational models.
Your point about gene therapy vs drug development simply makes no sense in my opinion.
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u/Bubbyjohn Oct 14 '24
Thatās because you are confusing gene therapy and drug development as two different things
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u/Bubbyjohn Oct 14 '24
Maybe you are not familiar with small molecule vs large molecule?
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u/thisaccountwillwork Oct 15 '24
No offense, but it doesn't sound like you actually know what you're talking about.
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u/Bubbyjohn Oct 15 '24
You said protein design isnāt close to modeling drug responses in humans.
Iām trying to say that proteins are the drug
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u/sibbydongdaday Oct 17 '24
Ya I donāt think the guy is arguing that ML designed proteins are POTENTIALLY great drugs, but ML only addresses a small part of drug development currently. And that small part is simply just designing the ādrugā and nothing more as of now. The next step in drug development would be figuring out what the drug does in vitro and subsequently in vivo. Ideally you want ML to completely bypass the in vitro and in vivo tests but unfortunately living things are way too complex and it is difficult to predict how the body responds to the drug. You might say that would be the exact reason why you need to implement ML, but I think itās difficult to actually do that since the body is so complex it is difficult to get quality data (or thatās my interpretation).
In essence, protein design is one amazing step forward, but that is just the first step in drug development.
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u/Bubbyjohn Oct 17 '24
Yes but itās not build on sand. I was simply agreeing with the person that he disagrees with. Itās not really Apple to oranges comparison to me. It honestly goes hand in hand, because you are developing proteins based on its response. Iāve seen stapled chains, I doubt AI suggested that. But that doesnāt mean that AI is not the very solid rock we are going to move forward on.
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u/fooliam Oct 14 '24
Yeah, it's gonna be a LONG time before AI models of human-drug interactions are anything like useful.
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u/bozzy253 Oct 13 '24
I think this is a great example of an amazing tool that creates interesting academic experiments in a tube. Moving from a cool experiment to human biology is a giant leap. I truly hope Iām wrong, but thereās just so much we do not know about biology that isnāt captured in silico.
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u/The_Infinite_Cool Oct 14 '24
like the old dark European fairytale kind that requires a ridiculous amount of esoteric knowledge or dumb luck to function right
Dumb luck ain't science
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u/Auzzie_almighty Oct 14 '24
Luck is decent part of life science: Mutagenesis is literally just mutating things as random and hoping it works out, penicillin was discovered by a chance contamination of an agar plate, and the HEK293 line was made by a guy brute forcing adenovirus dna into embryonic kidney cells until he got lucky and it worked.
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u/bluesquare2543 Oct 13 '24
luddites who don't understand the technology.
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u/Charybdis150 Oct 13 '24
Not luddites, just people with an understanding of the drug development process. No one doubts that AI can help with the discovery phase, the real doubt comes in at every stage after that. Letās say for the sake of argument that AI platforms can result in 100 times more candidate drugs than traditional discovery. Companies donāt have the time or money to bring all those candidates through preclinical and then clinical trials. Theyād have to pick and choose as a matter of practicality. As it is right now, no one sees a clear path to AI being able to predict how drugs interact with a complex biological environment not just in efficacy but in safety, so I see no reason to expect the 90% failure rate seen in traditional development to change with AI driven discovery. So while AI may have a very specific impact on the whole process, itās not likely to overall increase the number of actually approved drugs in my opinion.
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u/Inspector330 Oct 13 '24
This is the reality - I do not understand how anyone can believe otherwise at this point in time. Either the people investing are totally clueless or massive fraud is occurring. This is probably the result of privileged kids with 0 experience and knowledge of applicable science being involved in these investment decisions, coupled with dishonest founders and business men looking to boost their wealth/stock.
We lack an almost complete understanding of a cell and it's biology. What we know, or think we know, is not even a drop of water from oceans of knowledge. How can a model be built on shaky and comparatively severely limited data. As bozzy253 said, the language models are still garbage despite having the enormous amount of data it was built on. The AI model was actually better in earlier iterations - seems to get worse with time by the creators trying to hide its flaws. And to think there are people who believe AI will take over the world.
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u/catman609 Oct 14 '24
The reality is that Ab initio calculations can be performed on standard hardware, albeit slowly. Recent research demonstrates that deep learning can predict these ab initio calculations with remarkable accuracy (https://arxiv.org/pdf/2405.04967v1) using significantly less computation. This is a straightforward application of a machine learning techniques. Arguably, obtaining this data is simpler than curating the datasets used to train large language models like ChatGPT.
In fact you have a perfect reinforcement learning problem. Which has been proven to be the most successful approach i.e alphaGO, alphaStarcraft, etc. If the same resources were given to these applications we could have a defining moment in how science is done.
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u/momo-official Oct 15 '24
Well said. We need a LOT more research data to produce high-quality, reliable models. I'm always iffy when some flashy software claims to map interactions with high accuracy.
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u/pubeyy Oct 13 '24
The only good examples Iāve seen of AI in my work (MA/HEOR) is of summarising complex material. The issue though is often you need to read the complex material anyway so itās not really saving you any time unless youāre lazy and/or blagging that you know the technical details (which is often an issue with colleagues!)
Thereās definitely some good opportunies now where you can feed in a CSR or GVD into a companies tool and ask it to generate a summary on a particular endpoint or market
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u/TabeaK Oct 13 '24
As usual, Derek Lowe has a good blog about it: https://www.science.org/content/blog-post/ai-and-biology
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u/Eren-Sheldon-99 Oct 13 '24
Not an AI expert but I think high expectations leads to disappointment.
In my opinion, AI can help with niche well-defined projects with high quality data. It will improve drug development and reduce failure but maybe not in a magical way.
Maybe instead of 1% chance of clinical translation. You'll have 5% chance.
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u/FuB4R32 Oct 13 '24
I think this is exactly it.Ā A 1% to 5% is still a 5x improvement, and shouldn't be discounted.Ā It definitely works but won't solve all problems in the next 10 years let's say
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u/2Throwscrewsatit Oct 13 '24
Itāll save on administrative and regulatory costs real quick. Youāll see far fewer jobs in those sectors moving forward.
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u/Pellinore-86 Oct 13 '24
There likely isn't enough high quality data to sufficiently power a good biology LLM or a comprehensive structural one (alphafold is a small fraction of the proteome). Next, consider that 60% of that input data may be wrong or only conditional true.
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u/omgu8mynewt Oct 13 '24
Didn't AI for protein structure prediction, which contributes to drug development, win a Nobel prize less than a week ago? Just because it isn't making money hand over fist doesn't mean it is a waste of money. Individual cases of companies using AI are as nuanced as any other type of new company, and all rely on keeping investment interest until they become profitable so are motivated to sound interesting to investors.
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u/glr123 Oct 13 '24
Lots of Nobel prizes are misguided. As someone in this field, it might be one of the more obvious missteps. It's really hard to rationalize how AlphaFold in particular is worthy of a Nobel. David Bakers work, sure, but that's less about this kind of AI drug discovery modeling.
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u/jlpulice Oct 13 '24
also even in the case of Baker, itās more proof on concept than actual results correct? so much of the language in those announcements was ācould beā or such, seemed hard to point to non-academic outcomes?
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u/padakpatek Oct 13 '24
computational teams in industry definitely use the Rosetta suite of tools which his lab developed
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u/AppropriateSolid9124 Oct 13 '24
i am, in my deepest core, an AI hater, but alphafold revolutionized structural biology. alphafold provides a good starting point for creating protein structures, which used to be an incredibly long process. itās definitely still a while, but a matter of weeks/months after creating a crystal instead of years
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u/glr123 Oct 14 '24
AlphaFold in no way revolutionized structural biology... You can do almost nothing de novo with it, and at best it's good at really advanced pattern matching. I've used it and benchmarked with my own crystal systems and outside of relatively simple use cases it has not been impressive. I work on drugging large protein complexes and it is completely inept in that realm.
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u/serialmentor Oct 14 '24
I disagree. Here is a paper where the authors de-novo designed peptide binders, with shockingly high success rates: https://www.biorxiv.org/content/10.1101/2024.09.30.615802v1
Importantly, this was only possible because they could back-propagate errors through the AlphaFold network. You could not implement the same approach with something like Rosetta.
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u/glr123 Oct 14 '24
I don't think peptide binding is really the same as protein folding, especially when you're just trying to do pattern matching to fit a particular sequence to a surface. That's very different than de novo protein folding or finding new architectures in its entirety.
Even still I don't think that paper makes it anywhere near Nobel worthy when lots of other tools have done similar things over the years.
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u/AppropriateSolid9124 Oct 14 '24
yeah itās not great with that tbh. Iām still in academia, so its huge for academics as a baseline
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u/thenexttimebandit Oct 13 '24
Having a starting point for structure based drug discovery is incredibly useful. Obviously proteins can move when there is a ligand bound but itās still super helpful.
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u/halfchemhalfbio Oct 13 '24
That just half the equation and I hope we are better now. I still remember in the 2000s that Dave and my PI did a SAR development and found a drug. It works and shows activity but after crystal structure validation, the binding is opposite of predicted confirmation.
The bigger problem that need to be solved is not the drug design part but at finding novel targets that we miss with human intelligence like whatās the target for Alzheimerās disease etc.
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u/Cultural_Evening_858 Oct 13 '24
Wasn't there work with Priscilla Chan's Virtual Cell?
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u/halfchemhalfbio Oct 13 '24
Zuckās wife? Well she has money to burn so it probably will work eventually. Got to hire the right people though, I see a lot of AI companies hire engineers and people absolutely know zero biology or drug discovery. A lot big talkers but with feet under water, just my opinion of course.
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u/Cultural_Evening_858 Oct 14 '24 edited Oct 14 '24
It seems like companies are looking for some elite "hacker genius" with a software degree, but in doing so, theyāre missing out on a valuable and potentially more affordable talent poolālife science majors who can code. At this point, Iām not even sure if itās worth it for us life science majors to pursue Machine Learning Engineer roles, even if a job opens up. The burnout must be intense, especially when small teams rely on these so-called hacker geniuses to handle all the work.
I'm just trying to get stronger before I make the leap back to being an ML eng. I feel like with the amount of stress and how hard they make these interviews, the pay should be at least double what you make in biotech though. They post these low salaries and expect the world.
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u/Neother Oct 13 '24
In a well defined narrowly focused applications like protein folding, machine learning can give us useful results because the problem is well defined and we have lots of data in a clear format from sequence to structure. AI will continue to help in similar well defined niches, but the challenges of drug development are so much harder because there's unknown unknowns, biological feedback loops, dynamic molecular interactions we struggle to simulate, judgement calls about how severe side effects can be while approving a drug for use, and so many other nuances. If you break the problems down, there are ways to incorporate deep learning in specific domains similar to what Alphafold did for protein folding, but a lot of the core problems are very much based in problems AI is poorly suited to solve. Many drugs fail without a clear reason and expecting AI to magically figure out why some drugs failed and some succeeded when researchers themselves don't always know why is just pure hope. At the end of the day AI is just statistics and if you can't substitute the buzzword out your application probably doesn't make sense.
e.g. a statistical method to identifying likely conformations of folded proteins outperforms molecular modeling approaches (alphafold)
Vs
a statistical method will contribute to speeding up drug development (?????)
Make no mistake, the tech is VERY useful and not going away, but without a clearly defined problem space, it hasn't yet gotten to the level where it can do much more than act as an efficient reference text for human researchers.
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u/TabeaK Oct 13 '24
Protein structure is (relatively speaking) a simpler problem. There are only 20 amino acids (ignoring the engineered ones here) and protein structure is largely driven by physicial requirements of being in an aqueous or lipid rich environment. We happen do understand those rules well, we happen to have reasonably clean annotated data (PDB). We have none of those things when it comes other areas of cell biology...
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u/genome-gnome Oct 14 '24 edited Oct 14 '24
I work in and did my PhD in this space. My opinion is it has potential for specific, well framed questions and datasets. However, the trend I see is very few companies are actually taking the time to iterate on the question and collect a dataset to answer it. Especially in big pharma, the desire is to dump all sorts of random archival data into the magic algorithm and expect to get some miracle result. In this way, I think ChatGPT really broke the brains of leadership, as now they think weāre just moments away from the drug discovery equivalent and thatāll bypass a lot of the typical hurdles (and data generation costs) of R+D. I think thereās a real risk of not investing in AI the right way, and not seeing the benefit. IMO some startups are on the right track, but weāll see what pans out in time.
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u/SuperNewk 24d ago
Quick question! How much data is being creating on modeling new drugs, given the statistics that maybe 1% chance youāll have a clinical translation itās above 6-7% not amazing but still a huge jump. Iād imagine so much data being generated from these types of models might cause an issue in terms of storage going forward?
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u/thenexttimebandit Oct 13 '24
AI ADME models work pretty well and save a ton of money. The AI models to evaluate ligand binding are a work in progress but are a useful first pass before using more computationally expensive models. AI isnāt going to discover a drug because there are too many variables but it can be useful to guide drug development.
Edit: this is all focused on small molecules. Behave no idea how AI will fare for large molecules but my guess is not well.
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u/TabeaK Oct 13 '24
You mean predicting tox risk basked on structural similarity? Not sure I'd call that AI, but it doesn't and won't save you the expensive cyno studies and phase I...
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u/thenexttimebandit Oct 14 '24
You can build ML models to predict certain adme parameters based on structure but it wonāt even replace rat PK let alone cyno or phase I. Itās a useful tool but not going to fundamentally change drug discovery for a long time.
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u/notactuallyabird Oct 13 '24
It takes 10+ years to bring a drug to market so we wonāt know the impact of AI on drug design for quite a while yet.
My personal view is that we will see some āAI-designedā drugs hit the market in that timeframe, and maybe that makes it a fine enough investment, but I doubt itāll live up to the (enormous) hype.
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u/frazzledazzle667 Oct 13 '24
I've seen some AI biotech companies succeed and some look like they don't know what they are doing. AI is a tool, when you understand it and provide it good data it can be incredibly powerful. If you half ass it and provide bad data you're just going to get bad data out.
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u/benketeke Oct 13 '24
For structure prediction of monomers or antibodies, AI is golden. Donāt really need a crystal structure anymore.
I believe all information to be extracted to link evolution to structure has already been extracted. For design, we can relatively easily design things like small peptides with helices etc. that bind to a target (think no phage display needed).
What we donāt get yet, is a molecule thatās ready for Phase 1. It still needs a lot of work to become a viable drug.
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u/Torontobabe94 Oct 13 '24
Definitely built on sand, they have no idea what theyāre doing (in biotech regarding AI) and throwing as much money as they can at it, so they can say they were the first one to do X or Y
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u/persedes Oct 13 '24
AI/ML has usage in biotech like a plate reader or a pipet. It is a tool that can massively upscale your throughput and enable certain experiments that you could not otherwise. People have been doing "AI" for 10+ years to aid drug development and people will still claim that, but take it with a grain of salt. AI won't magically run experiments for you
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u/TheNightLard Oct 13 '24
Don't think about what ChatGPT has done for drug development (which is a lot but hardly marketable and mostly indirect), but think what Google DeepMind has done. No idea about the investment there, but it has passed every single pharma, big or small, on the right and is far down the road, and getting further away by the minute.
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u/hahdheisnz Oct 13 '24
AI is revolutionising high-throughput drug discovery pipelines. instead of picking out "starting ingredients" on a hunch, we are now able to predict likely useful candidates, saving loads of time. Developments in machine learning are also helping us deconvolute results to pick out strong drug candidates that would otherwise be missed as false positives or negatives due to things like low-yield reactions and reagent contamination. Look up the use of AI in D2B pipelines, for instance. I'm sure there are plenty more examples.
The best is yet to come.
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u/Cultural_Evening_858 Oct 13 '24
If there is an AI biotech that is going to make it and Pre-IPO please let me know? in the meantime, what training courses should I do to become a stronger machine learning engineer?
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u/thisaccountwillwork Oct 13 '24
Not to be rude but it sounds more like you are still in the figuring out phase of how to be an ML eng in the first place. Get up to speed with the average person in the field and by then you should become aware of what you want and need to do to get ahead.
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u/Cultural_Evening_858 Oct 14 '24
Thanks for the feedback. I used to work in ML back in 2019, but I burned out after a while. My employer at the time wasnāt supportive of learning on the job, which was fair. Iāve realized that I need to be more disciplined in my self-study now.
Since you seem familiar with the field, could you suggest some resources? I'd appreciate any book recommendations, repos to explore, or specific areas to focus on to get back up to speed. I recently downloaded AlphaFold and got it working, but Iām looking for more practical skills or projects that would not only help me become more employable but also make me more resilient the next time I take on an ML engineering role.
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u/Weekly-Ad353 Oct 13 '24
Yes.
It always has been if youāve actually looked.
Assuming you werenāt trying to sell it yourself.
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u/easy_peazy Oct 13 '24
I donāt think AI will be an end to end solution or one stop shop for drug development but Iāve seen it really perform nicely in limited use cases where data is available.
I think this will actually be bigger because smaller, more limited (but still useful) AI models will be integrated into every step of drug development process. Each application will incrementally improve efficiency and get better over time. This is the foundation for a successful AI industry in my opinion.
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u/ShadowValent Oct 13 '24
Wait until AI starts giving sponsored responses. It will absolutely happen.
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u/Safetym33ting Oct 14 '24
I've noticed a few articles about a.i. being extremely useful in detection of cancers.Ā Hopefully this actually "fleshes out".Ā
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u/bars2021 Oct 14 '24
Right now small molecule is what needs to be tackled, once this can be achieved the industry can then move onto large molecule.
Within SM we need to work on a multi parametric approach to save valuable laboratory time (BB barier, metabolism, toxicity, able to be synthesized in the lab, other ADMET properties etc..) Think predictive AI for now then when this is proven we could move on to generative AI. It's going to take lots off work, lots of medicinal chemists validating in the wet lab but we'll get there:).
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u/Competitive_Post8 Oct 14 '24
from what Nvidia CEO said, they will.. figure it out with new AI apps called agents.. once they figure it out, they will release these tools for people to use. so my point is useful ai has not been delivered yet, but it is being planned.
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u/HearthFiend Oct 14 '24
I seen some impressive things at Genetech by one of the AI subdivision, they use it to optimise existing candidates which looks pretty promising?
Ive no idea what happened to it but the fact they can optimise protein yield with AI assisted engineering surely means something.
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u/ToePotential3707 Oct 14 '24
Highly depends on the application - lots of promising stuff in development but just as drugs take a. Long time to validate, the same is true about AI - Esp considering the regulatory frameworks.
Have seen some AI models fda approved in my space with various applications - have also personally seen large pharma get very excited for co development AI projects we are developing.
The number 1 most important think is defining the use case up front - which 99% of people don't know what they truly want unless they can talk through their use cases with developers.
I.e Predicting response is not specific enough
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u/Mission-Health-9150 Oct 15 '24
I get the skepticism, itās fair, given the hype and underwhelming results so far. AI in drug development isnāt perfect, but itās not just smoke and mirrors either. It shines in areas like molecular screening, predicting drug-target interactions, and reducing R&D timelines.
Failures are part of the process, just like with traditional methods. The real value might not show immediately, but small wins, like identifying better leads faster, are already happening. Itās not a silver bullet, but more like one tool in the toolbox thatāll improve over time. Itās early days, and the potential is there, even if the roadās bumpy rn.
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u/4dxn Oct 18 '24
lol i always find it funny people only think of llms when they think of AI. AI has been in use in drug dev for decades. some of the earliest applications of AI have been in healthcare. dendral was a project in the 60s to identify organic molecules. and if you expand to healthcare, they used in imaging for forever.
llms are a small fraction of the AI discipline. thats like equating mrna with drug development because it got so much hype due to covid.
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u/Sakowuf_Solutions Oct 13 '24 edited Oct 13 '24
All in silico technology is
š
Edit: what? This is funny because itās TRUE.
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u/nel_wo Oct 13 '24
I know lilly uses AI for drug discovery. We have AI models that reconstruct pharmaceutical and protein molecular structures to test if drugs would work.
We also use AI modeling and testi g for new drug development to create or modify different molecules so it can led to increase uptake or cross through cellular barriers more easily.
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u/HavocHybrid Oct 13 '24
This is the same way its being used in Pfizer and BMS. I would assume all BigPharma are using it for Drug Discovery and Clinical Trial modeling.
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u/Iyanden Oct 14 '24
AI as a tool for disease prediction for covariate adjustment in clinical trials (e.g., insitro) is one of the good use cases for AI in drug development right now. Conceptually, it's like shrinking confidence intervals which can be translated to increasing sample size/power which can be translated to a dollar amount.
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u/Scottwood88 Oct 14 '24
I'm bullish on there being major breakthroughs driven, in part, by AI within 50 years. That's a long time window- think of how many inventions today didn't exist back in the mid 70's. I'm just not sure of the immediate benefit within these next few years. I think it still costs way too much to run the models, there needs to be better data and more people need to get trained and educated on infusing AI with software. It feels like a long term play that is in the early innings.
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u/ForeskinStealer420 Oct 14 '24
Individual components of the process can be effectively done with AI. For example, the AlphaFold algorithm is great for predicting the folding conformation of amino acids; this can be a powerful tool for screening/modeling candidate drugs (in the early stages before simulations, in vitro, etc).
Can AI handle the exhaustive set of drug discovery/development problems? Not yet. Anyone telling you otherwise is an overly optimistic venture capitalist.
āAI in drug discoveryā is in a massive hype period. There are dozens of companies and startups doing it; most are BS, and a small handful are good (ex: DE Shaw Research).
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u/ProteinEngineer Oct 14 '24
It depends what you mean by AI in drug development. It did just win the Nobel prize last week.
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u/kinnunenenenen Oct 13 '24
I think comparing chatGPT To AI/ML for biotech isnāt useful. The former is a generalist chatbot meant for public consumption and to impress funders enough to keep money coming in. I think ML in biotech is going to manifest as a set of specific tools for specific challenges. So for instance itās already helping understand protein structure, which is useful for a wide range of things like vaccine design or understanding Ab binding. Active learning tools are being used to optimize biofuel titers (my field) and I know companies like BigHat have had success with active learning for Ab optimization. Recursion (and others!) is doing ML for (hopefully) learning from massive datasets. ML for microscopy is super useful for segmentation and image restoration. Some of this might be vaporware but some of it is certainly helping already and will continue to help understand biology.