16 ms·

AlphaFold-Powered Drug Discovery of a Novel CDK20 Inhibitor

173 points by in3d 5y ago

deleted 5y ago
[deleted]
virgilp 5y ago
Can someone explain for people who aren't good at biology what are the implications of this "novel CDK inhibitor"? Is this news only because of the discovery method (i.e. AI-based), or is it significant news in and of itself (i.e. this novel CDK20 inhibitor is/could be a big deal)?
- feanaro 5y ago
  CDK inhibitors play a role in cancer therapy.
  - vmception 5y ago
    Would a greater amount of nonspecific cancer funding have allowed this specific discovery to be found faster since it is limited by a machine and human evaluation of the results?
    - CryptoPunk 5y ago
      Wouldn't all discoveries have likely been found faster witb greater funding?
      - vmception 5y ago
        No. Not at all. Too many cooks in the kitchen makes diminishing returns.
  - kvetching 5y ago
    You say they play a role in cancer therapy, yet the paper states, "this molecule is the first reported CDK20 inhibitor". How could they have played a role before they existed?
    - tazjin 5y ago
      CDK = cyclin-dependent kinase CDK20 = a specific CDK
- in3d 5y ago
  I think this is explained pretty well in the paper: "...hepatocellular carcinoma (HCC) was nominated as the indication of interest due to its high prevalence in liver cancers and lack of effective treatments. In general, by analysis of text and OMICs data from 10 database for hepatocellular carcinoma, PandaOmics provides a top list of 20 targets after multiple dimensions filtration, including novelty, accessibility by biologics, safety, small molecule accessibility, and tissue specificity. CDK20 was finally selected as our initial target to work on due to its strong disease association, limited experimental structure information and with no publicly small molecule inhibitor. [...] To the best of our knowledge, this molecule is the first reported CDK20 inhibitor and moreover, this work is also the first reported example which successfully utilized AlphaFold predicted protein structures to identify a confirmed hit for a novel target in early drug discovery"
  - onlyrealcuzzo 5y ago
    I'm too dumb to understand that. For people like me, after some Googling, I got: hepatocellular carcinoma is a rare form of liver cancer (affecting less than 200k people in the US per year). CDK20 is strongly associated with that cancer / disease. This molecule inhibits CDK20 - so it might help people with hepatocellular carcinoma. But I'm dumb and know nothing, so that someone correct me if I'm completely wrong. I tried my best ¯\_(ツ)_/¯
    - drocer88 5y ago
      "HCC is the third leading cause of cancer-related deaths worldwide."[1] [1] https://en.wikipedia.org/wiki/Hepatocellular_carcinoma https://en.wikipedia.org/wiki/Hepatocellular_carcinoma
    - busyant 5y ago
      The researchers focused on a particular type of liver cancer (HCC) because of "lack of effective treatments" They focused on a particular protein molecule called CDK20 which appears to be important for development of HCC. You can think of it this way... "If CDK20 goes 'haywire', it can contribute to the development of HCC." The idea is that if you can stop CDK20 from going haywire, perhaps you can slow/stop/prevent/reverse development of HCC. Along those lines, if you can find a "small molecule" that stops CDK20 from going haywire, that small molecule could potentially serve as a medicine for treating HCC. "Small molecule" is a common pharma term for molecule that is smaller than most biological "macromolecules" like proteins. "Small molecule" is often used (roughly) interchangeably for a molecule that can be developed into an ingestible pill (or injected). "Small molecule" medicines are often relatively stable (i.e., can be stored for a long time without many restrictive storage conditions), cheap to manufacture (not always), etc. These people used computational methods to create several candidate small molecules that might stop CDK20 from going haywire (the 'technical term here is that the small molecule inhibits CDK20. Some of this was done in conjunction with AlphaFold's predicted 3-D structure for CDK20. But just because the computer says that your small molecule might inhibit CDK20, that doesn't mean that your small molecule will _actually_ inhibit CDK20 in the real world. The first _true_ test is to make the small molecule and experimentally assess whether it inhibits CDK20. One of their candidate small molecule compounds appears to inhibit CDK20. That's one of the punch lines of the paper. ==================================== I don't like it when people are too negative about this stuff, but I'm going to be a little negative here. Other than employing AlphaFold, this seems like pretty standard work for pharmaceutical development. I worked for a company doing structure-based drug design and the general concepts employed in this paper are not different from what wewe (and others) have been doing for a while. They have a particular platform for finding these types of molecules and they would like to argue that their platform is unique and distinguishes itself from everybody else's platform. I'm not saying that the candidate small molecule is "bad" or anything like that. It's definitely a promising lead and it needs to be pursued. But it's just a lead. A lot of biotech / pharma involves hyping/selling your particular drug discovery platform and trying to convince people that your platform is the better/more efficient way of finding valuable drugs. Just my 2 cents.
- mutlimind 5y ago
  Finding a molecule that inhibits the use of the gene CDK20 could potentially help to stop certain cancers/tumors to grow and maybe even stop them to circumvent certain parts of the immune system
- threeseed 5y ago
  I have zero understanding of biology but apparently "Diseases associated with CDK20 include Obsessive-Compulsive Disorder and Attention Deficit-Hyperactivity Disorder". https://www.genecards.org/cgi-bin/carddisp.pl?gene=CDK20 https://www.genecards.org/cgi-bin/carddisp.pl?gene=CDK20
- nik_s 5y ago
  Regarding the biology: - One of the strategies in drug development is to find a protein that is more present in people who have a disease versus those who don't (called "overexpressed" proteins), and attempt to stop this protein from work correctly (called "inhibition") by having a small molecule drugs that binds to a specific site of the protein (called the "allosteric site") in order to make it mechanically unable to execute its function. - Cyclin-dependent protein kinases (CDKs) are a family of proteins that seem to play important roles in controlling cell division. CDK20 is overexpressed in a number of cancers. Regarding the novelty: - Discovering new inhibitors of proteins based on AI is definitely less novel than it was 5 years ago - while it's definitely still not the norm, AI is making big waves in the pharmaceutical industry. However, I think this might be the first publication validating the use of Alphafold for small molecule drug development, which is a major step forward. - While it's interesting to see that it's possible to design a small-molecule CDK20 inhibitor, it's currently still very uncertain whether this is a promising drug: i. the compound could be insufficiently specific to CDK20 and could bind to other important proteins and cause unwanted and potentially serious side-effects, ii. the compound could have bad "drug-like" properties (e.g. bioaccumulate in the liver) or be toxic in some way, iii. the compound could interact badly with other drugs that cancer patients receive, iv. the compound could induce resistance (a common problem in small molecule drugs in oncology), and finally, and most importantly, v. the drug might just not be effective at treating cancer or any other diseases - it's not because a protein is over-expressed that it's the cause of a cancer, but rather a symptom of another biological dysregulation. Still, it's definitely an achievement, and I applaud the efforts of the team and hope they'll find successful treatments.
RandomLensman 5y ago
This could be an interesting addition to other large scale drug discovery methods. Not totally sure how orthogonal it is to other methods and about the price point when use at scale - but nevertheless. I suppose the critical issue will be still that going from molecule to treatment, the success rate is annoyingly low. (And, yes, there are lots of attempts to increase the odds here.)
- bayesian_horse 5y ago
  My impression was that arge scale drug discovery methods have largely failed....
  - _Wintermute 5y ago
    Apart from all the new drugs discovered over the past 30 years...
  - RandomLensman 5y ago
    Success rate is really low from an end-to-end perspective, but I would not call this a failure (and I expect the same from this approach). It reflects the difficulty of the task.
bawolff 5y ago
For people who know about this area: How far away is something like this from an actual treatment? I assume its pretty far, but is it the sort of thing where you basically have to test to see if it works and is safe-ish? Or are a lot more steps involved? What does promising in this context mean? Like is it the sort of thing that has a 50% chance of eventually being useful, or is it more like 1% chance? Regardless exciting to see alphafold be useful!
- kettleballroll 5y ago
  Very, very, very far away. This paper is about target selection and hit identification, the very first steps of the pipeline. What follows is Backbone optimization (tweaking the chemistry, and figuring out how to actually produce it), pre-clinical trials (in cells and later in animals), and should those prove successful (fairly unlikely in general), then we'll have clinical phases 1-3. Each of those will take several years. Most candidate drugs that even manage to come to clinical trials will fail there (i forgot the exact numbers, but far over 95%). In general: this research is at the stage of someone coming to a software engineer with the words "I have an idea about an app". The actual work hasn't really started yet, and most likely this won't work at all. Should this molecule be successful (Wich is highly unlikely, as most hit targets aren't) it will probably take an decade until this becomes an experimental treatment, and likely two or more until it becomes a standard treatment. All in all, this isn't really big news at all, this is a very unexciting thing, and only gets views because it has AlphaFold in the title (Caveat: it's been a few years since I worked in a remotely related area)
  - pfdietz 5y ago
    I don't think 8.9 uM is a terribly good active concentration for a drug, so there is a lot of work to be done here.
    - _Wintermute 5y ago
      I know CDK20 is a difficult target, otherwise presenting results where your best hit is at 9μM would be pretty disappointing. It would be interesting to see the Kd values for the other CDK family members, there's no mention at all of specificity in their paper.
rguiscard 5y ago
Several authors are in Insilico Medicine[1], which uses AI for drug discovery and has one product in clinical trials if I remember correctly. [1] https://insilico.com/ https://insilico.com/
- bayesian_horse 5y ago
  This paper doesn't use Machine Learning at all, as far as I understand it.
  - fnands 5y ago
    It does. They used Chemistry42 which is Insilico's ML based molecule generation platform(?)[0]. Sure, they didn't train any models specifically for this case, but they used a protein structure predicted by one ML algorithm, then used another set of ML algorithms to find and rank molecules that might match the protein, and then tested the most likely ones in reality. [0] https://arxiv.org/pdf/2101.09050.pdf https://arxiv.org/pdf/2101.09050.pdf
jimmySixDOF 5y ago
Interesting to consider this in light of yesterday's news about the lack of success for IBM Watson in Healthcare. https://news.ycombinator.com/item?id=30046432 https://news.ycombinator.com/item?id=30046432 IBM’s Watson Health is sold off in parts
- fnands 5y ago
  Eh, I would argue there is a massive difference between drug discovery and patient care, which was what Watson was aiming for. Filtering out useful molecules from useless (or dangerous) ones is a nicely contained (if difficult) problem that ML is pretty good at solving.
  - ramraj07 5y ago
    To say Watson was aiming for anything is an injustice to the word Aim.
    - Closi 5y ago
      How about "Watson was aiming to sell consulting services"?
    - TaylorAlexander 5y ago
      Aiming to market IBM as cool.
  - bayesian_horse 5y ago
    AlphaFold doesn't sort out molecules. This paper took the 3D model of a protein predicted by the original AlphaFold paper and used that to test random small molecules against it, first virtually then physically.
- epistasis 5y ago
  Alphafold has a clear, easily stated application and problem it solves. Watson, like the AI craze in general, was mostly buzzwords and hype covering the fact that it was actually three application specialists in a trench coat trying to pass themselves off as machine intelligence.
mikkelam 5y ago
Offtopic: More and more I'm noticing the ever-increasing amount of chinese names in machine learning papers. Can anyone explain that? Are these researchers mainly choosing this field out of their own interest, or is China somehow pushing/sponsoring a lot researchers to pursue this field? I realise that China is pursusing AI dominance, is that just what we're seeing? Note this is purely curiosity, I have no problems with the Chinese people (CCP is a different story).
- bawolff 5y ago
  I don't think there is any big mystery. AI is a hot area, and a reasonable percentage of smart people have Chinese ancestry (regardless of where they reside now).
- captainmuon 5y ago
  Well first of all, China is a huge country with four times the population of the US or three times of the EU. The greatest "trick" that they have is that they count as one country. It would look different if they were a continent made up of a dozen (still pretty big) countries. I used to work at a Chinese research institute (in a non ML-field) and the team leader took some time and frequently encouraged his students to learn ML, to try to apply it to their work or just for the sake of it. He was very open about the fact that it would benefit the nation, but that also ML researchers were in good demand and it would be great for the student's individual careers. So yeah it is mainly just a very popular topic over there, too. But in addition, I feel that China (the gov/party/research community, whoever) thinks more strategically than for example the EU.
- Jyaif 5y ago
  You should see it more as China finally starting to pull its weight in scientific research. Their population is 4x that of the US, yet their scientific impact is AFAICT less than that of the US.
- cracrecry 5y ago
  >Can anyone explain that? It is very simple to understand: There are more than 1.200 million Chinese people just in China, and a couple hundred million more outside. AI is also growing and creating new opportunities, most of them work in Universities on the West. The same happens in other fields like piano playing, there are so many exceptional players because there are so many Chinese.
londons_explore 5y ago
This paper looks more written for investors than as a scientific process for others to work from...
- MeteorMarc 5y ago
  Yes, I do not know how to feel about this. On the one hand it is great that they found this and made the results public before trying any followup microbiological research. On the other hand, they seem to say: we, engineers, did in two years what you, scientist, failed in doing the last 20 years. They could have just written a clean scientific paper and mention AlphaFold in their methodology section.
didntreadarticl 5y ago
It would be great it we could find a way to use Alphafold to kindof reverse engineer proteins - like to specify the shape you want and then run Alphafold 'backwards' so that you're going from shape -> DNA instead of DNA -> shape
- bayesian_horse 5y ago
  They mention a framework for protein design. But I guess you'd start out with a know sequence/structure and mutate it towards what you want.
  - gilleain 5y ago
    Yes, a common way to design new structures is to 'thread' the sequence onto existing folds. Rarely, there have been attempts to engineer entirely new folds, although it's not clear how necessary that is.
    - gusennan 5y ago
      Early evidence seems to show that AlphaFold has trouble with single point mutations that change a protein's shape, so completely de-novo proteins will be a challenge: https://www.nature.com/articles/s41594-021-00714-2 https://www.nature.com/articles/s41594-021-00714-2
      - dekhn 5y ago
        I would not expect any program to reliably predict the effects of single mutations that massively change the protein's shape unless there was enough high quality structural data and sequence data for both substates and enough signal to predict which substate the protein would adopt after mutation. Fortunately, evolution already encoded robustness against this sort of problem into proteins and the vast majority of single point mutations are tolerated (the resulting enzymes are often nearly as active and stable as the originals).
        5y ago
civilized 5y ago
Very cool. It's hard to read the tea leaves of the author affiliation list, but it sure looks like this work was led by a Hong Kong-based startup founded by Russians, with limited assistance from a couple North America-based researchers. I'm curious why those Russians may have chosen to found in HK rather than, say, the Bay Area. We may want to contemplate what this means for America's competitive stance (not that I have any problem with the rest of the world doing awesome cutting edge research).
- londons_explore 5y ago
  It's pretty hard for Russians to get US visas for startups. They probably went to HK because that's a far easier place to build a team.
  - civilized 5y ago
    Russians in particular?
    - londons_explore 5y ago
      I don't know if you've heard, but Russia and the USA haven't been best buddies for quite some time...
      - lajamerr 5y ago
        Have they ever?
        hangonhn 5y ago
        Yes! During the days of the Russian empire, especially during the Civil War. They were staunch supporters of the Union although their own position on their serfs made that support a bit awkward.
      - civilized 5y ago
        Neither are US and China, but we still have tons of Chinese immigrants powering our tech economy. Maybe they're coming more through student visas, which may be of less interest to the Russians.
cing 5y ago
I agree with the sentiment of this paper (AF can enable drug discovery), but in this specific instance, the authors had a real opportunity contribute a general finding to the scientific community but instead they put in the lowest amount of effort (to a point where they're almost saying nothing at all). The target had dozens of related structures in the protein databank, including relatives with ~40% sequence identity. This target family has a very similar structure, and conserved active site residues. It's relevant that this target has approved cross-CDK family inhibitors (and thousands of data points of CDK family binders on ChEMBL). The conventional way to enable structure-based design is to build a homology model using a similar structure (see here: https://swissmodel.expasy.org/repository/uniprot/Q8IZL9?template=1v0o.1.A&range=3-290 https://swissmodel.expasy.org/repository/uniprot/Q8IZL9?temp...), and in this case, there is very low deviation from the AF2 model and this "old fashioned" approach. To recap, this target had a decent model that would have likely sufficed for drug discovery. The community already knows that "homology models" can be used for structure-based drug design, so any methodological hypotheses of this paper are not supported by evidence.
- SubiculumCode 5y ago
  And this, dear HN community, is the difference between an expert reading a paper pertaining to their field and the casual reader, or even scientists in unrelated fields reading this paper. I am just not equipped to judge the quality of research in the field.
- dekhn 5y ago
  Although I agree the authors could have done homology modelling, in this case AlphaFold is already doing that. It knows all the related sequences (through the sequence database similarity graph that it embeds) and has a very sophisticated modelling system. In my guess (I'd have to check with my old friends to be sure) it does as well as if not better in producing atomic accuracy for structural predictions for homology modellers better than a typical modeller could produce. This paper is mainly a flag planted so they can claim they landed on mars first and fastest.
- jeejayisbusy 5y ago
  another important point to notice is affinity. While 8 uM looks impressive, it is not that hard to develop such potency since compounds are likely to aim ATP binding pocket. It is big, deep and offers many hydrogen bond donors in hindge region. What important for such compounds is selectivity, since you want to inhibit only specific kinase, not all of them. For me it looks like advertising of their platform, not actual scientific achievement.

173 points by in3d 5y ago

deleted 5y ago

[deleted]

Can someone explain for people who aren't good at biology what are the implications of this "novel CDK inhibitor"? Is this news only because of the discovery method (i.e. AI-based), or is it significant news in and of itself (i.e. this novel CDK20 inhibitor is/could be a big deal)?

feanaro 5y ago
CDK inhibitors play a role in cancer therapy.
- vmception 5y ago
  Would a greater amount of nonspecific cancer funding have allowed this specific discovery to be found faster since it is limited by a machine and human evaluation of the results?
  - CryptoPunk 5y ago
    Wouldn't all discoveries have likely been found faster witb greater funding?
    - vmception 5y ago
      No. Not at all. Too many cooks in the kitchen makes diminishing returns.
- kvetching 5y ago
  You say they play a role in cancer therapy, yet the paper states, "this molecule is the first reported CDK20 inhibitor". How could they have played a role before they existed?
  - tazjin 5y ago
    CDK = cyclin-dependent kinase CDK20 = a specific CDK
in3d 5y ago
I think this is explained pretty well in the paper: "...hepatocellular carcinoma (HCC) was nominated as the indication of interest due to its high prevalence in liver cancers and lack of effective treatments. In general, by analysis of text and OMICs data from 10 database for hepatocellular carcinoma, PandaOmics provides a top list of 20 targets after multiple dimensions filtration, including novelty, accessibility by biologics, safety, small molecule accessibility, and tissue specificity. CDK20 was finally selected as our initial target to work on due to its strong disease association, limited experimental structure information and with no publicly small molecule inhibitor. [...] To the best of our knowledge, this molecule is the first reported CDK20 inhibitor and moreover, this work is also the first reported example which successfully utilized AlphaFold predicted protein structures to identify a confirmed hit for a novel target in early drug discovery"
- onlyrealcuzzo 5y ago
  I'm too dumb to understand that. For people like me, after some Googling, I got: hepatocellular carcinoma is a rare form of liver cancer (affecting less than 200k people in the US per year). CDK20 is strongly associated with that cancer / disease. This molecule inhibits CDK20 - so it might help people with hepatocellular carcinoma. But I'm dumb and know nothing, so that someone correct me if I'm completely wrong. I tried my best ¯\_(ツ)_/¯
  - drocer88 5y ago
    "HCC is the third leading cause of cancer-related deaths worldwide."[1] [1] https://en.wikipedia.org/wiki/Hepatocellular_carcinoma https://en.wikipedia.org/wiki/Hepatocellular_carcinoma
  - busyant 5y ago
    The researchers focused on a particular type of liver cancer (HCC) because of "lack of effective treatments" They focused on a particular protein molecule called CDK20 which appears to be important for development of HCC. You can think of it this way... "If CDK20 goes 'haywire', it can contribute to the development of HCC." The idea is that if you can stop CDK20 from going haywire, perhaps you can slow/stop/prevent/reverse development of HCC. Along those lines, if you can find a "small molecule" that stops CDK20 from going haywire, that small molecule could potentially serve as a medicine for treating HCC. "Small molecule" is a common pharma term for molecule that is smaller than most biological "macromolecules" like proteins. "Small molecule" is often used (roughly) interchangeably for a molecule that can be developed into an ingestible pill (or injected). "Small molecule" medicines are often relatively stable (i.e., can be stored for a long time without many restrictive storage conditions), cheap to manufacture (not always), etc. These people used computational methods to create several candidate small molecules that might stop CDK20 from going haywire (the 'technical term here is that the small molecule inhibits CDK20. Some of this was done in conjunction with AlphaFold's predicted 3-D structure for CDK20. But just because the computer says that your small molecule might inhibit CDK20, that doesn't mean that your small molecule will _actually_ inhibit CDK20 in the real world. The first _true_ test is to make the small molecule and experimentally assess whether it inhibits CDK20. One of their candidate small molecule compounds appears to inhibit CDK20. That's one of the punch lines of the paper. ==================================== I don't like it when people are too negative about this stuff, but I'm going to be a little negative here. Other than employing AlphaFold, this seems like pretty standard work for pharmaceutical development. I worked for a company doing structure-based drug design and the general concepts employed in this paper are not different from what wewe (and others) have been doing for a while. They have a particular platform for finding these types of molecules and they would like to argue that their platform is unique and distinguishes itself from everybody else's platform. I'm not saying that the candidate small molecule is "bad" or anything like that. It's definitely a promising lead and it needs to be pursued. But it's just a lead. A lot of biotech / pharma involves hyping/selling your particular drug discovery platform and trying to convince people that your platform is the better/more efficient way of finding valuable drugs. Just my 2 cents.
mutlimind 5y ago
Finding a molecule that inhibits the use of the gene CDK20 could potentially help to stop certain cancers/tumors to grow and maybe even stop them to circumvent certain parts of the immune system
threeseed 5y ago
I have zero understanding of biology but apparently "Diseases associated with CDK20 include Obsessive-Compulsive Disorder and Attention Deficit-Hyperactivity Disorder". https://www.genecards.org/cgi-bin/carddisp.pl?gene=CDK20 https://www.genecards.org/cgi-bin/carddisp.pl?gene=CDK20
nik_s 5y ago
Regarding the biology: - One of the strategies in drug development is to find a protein that is more present in people who have a disease versus those who don't (called "overexpressed" proteins), and attempt to stop this protein from work correctly (called "inhibition") by having a small molecule drugs that binds to a specific site of the protein (called the "allosteric site") in order to make it mechanically unable to execute its function. - Cyclin-dependent protein kinases (CDKs) are a family of proteins that seem to play important roles in controlling cell division. CDK20 is overexpressed in a number of cancers. Regarding the novelty: - Discovering new inhibitors of proteins based on AI is definitely less novel than it was 5 years ago - while it's definitely still not the norm, AI is making big waves in the pharmaceutical industry. However, I think this might be the first publication validating the use of Alphafold for small molecule drug development, which is a major step forward. - While it's interesting to see that it's possible to design a small-molecule CDK20 inhibitor, it's currently still very uncertain whether this is a promising drug: i. the compound could be insufficiently specific to CDK20 and could bind to other important proteins and cause unwanted and potentially serious side-effects, ii. the compound could have bad "drug-like" properties (e.g. bioaccumulate in the liver) or be toxic in some way, iii. the compound could interact badly with other drugs that cancer patients receive, iv. the compound could induce resistance (a common problem in small molecule drugs in oncology), and finally, and most importantly, v. the drug might just not be effective at treating cancer or any other diseases - it's not because a protein is over-expressed that it's the cause of a cancer, but rather a symptom of another biological dysregulation. Still, it's definitely an achievement, and I applaud the efforts of the team and hope they'll find successful treatments.

RandomLensman 5y ago

This could be an interesting addition to other large scale drug discovery methods. Not totally sure how orthogonal it is to other methods and about the price point when use at scale - but nevertheless. I suppose the critical issue will be still that going from molecule to treatment, the success rate is annoyingly low. (And, yes, there are lots of attempts to increase the odds here.)

bayesian_horse 5y ago
My impression was that arge scale drug discovery methods have largely failed....
- _Wintermute 5y ago
  Apart from all the new drugs discovered over the past 30 years...
- RandomLensman 5y ago
  Success rate is really low from an end-to-end perspective, but I would not call this a failure (and I expect the same from this approach). It reflects the difficulty of the task.

bawolff 5y ago

For people who know about this area: How far away is something like this from an actual treatment? I assume its pretty far, but is it the sort of thing where you basically have to test to see if it works and is safe-ish? Or are a lot more steps involved? What does promising in this context mean? Like is it the sort of thing that has a 50% chance of eventually being useful, or is it more like 1% chance? Regardless exciting to see alphafold be useful!

kettleballroll 5y ago
Very, very, very far away. This paper is about target selection and hit identification, the very first steps of the pipeline. What follows is Backbone optimization (tweaking the chemistry, and figuring out how to actually produce it), pre-clinical trials (in cells and later in animals), and should those prove successful (fairly unlikely in general), then we'll have clinical phases 1-3. Each of those will take several years. Most candidate drugs that even manage to come to clinical trials will fail there (i forgot the exact numbers, but far over 95%). In general: this research is at the stage of someone coming to a software engineer with the words "I have an idea about an app". The actual work hasn't really started yet, and most likely this won't work at all. Should this molecule be successful (Wich is highly unlikely, as most hit targets aren't) it will probably take an decade until this becomes an experimental treatment, and likely two or more until it becomes a standard treatment. All in all, this isn't really big news at all, this is a very unexciting thing, and only gets views because it has AlphaFold in the title (Caveat: it's been a few years since I worked in a remotely related area)
- pfdietz 5y ago
  I don't think 8.9 uM is a terribly good active concentration for a drug, so there is a lot of work to be done here.
  - _Wintermute 5y ago
    I know CDK20 is a difficult target, otherwise presenting results where your best hit is at 9μM would be pretty disappointing. It would be interesting to see the Kd values for the other CDK family members, there's no mention at all of specificity in their paper.

rguiscard 5y ago

Several authors are in Insilico Medicine[1], which uses AI for drug discovery and has one product in clinical trials if I remember correctly. [1] https://insilico.com/ https://insilico.com/

bayesian_horse 5y ago
This paper doesn't use Machine Learning at all, as far as I understand it.
- fnands 5y ago
  It does. They used Chemistry42 which is Insilico's ML based molecule generation platform(?)[0]. Sure, they didn't train any models specifically for this case, but they used a protein structure predicted by one ML algorithm, then used another set of ML algorithms to find and rank molecules that might match the protein, and then tested the most likely ones in reality. [0] https://arxiv.org/pdf/2101.09050.pdf https://arxiv.org/pdf/2101.09050.pdf

jimmySixDOF 5y ago

Interesting to consider this in light of yesterday's news about the lack of success for IBM Watson in Healthcare. https://news.ycombinator.com/item?id=30046432 https://news.ycombinator.com/item?id=30046432 IBM’s Watson Health is sold off in parts

fnands 5y ago
Eh, I would argue there is a massive difference between drug discovery and patient care, which was what Watson was aiming for. Filtering out useful molecules from useless (or dangerous) ones is a nicely contained (if difficult) problem that ML is pretty good at solving.
- ramraj07 5y ago
  To say Watson was aiming for anything is an injustice to the word Aim.
  - Closi 5y ago
    How about "Watson was aiming to sell consulting services"?
  - TaylorAlexander 5y ago
    Aiming to market IBM as cool.
- bayesian_horse 5y ago
  AlphaFold doesn't sort out molecules. This paper took the 3D model of a protein predicted by the original AlphaFold paper and used that to test random small molecules against it, first virtually then physically.
epistasis 5y ago
Alphafold has a clear, easily stated application and problem it solves. Watson, like the AI craze in general, was mostly buzzwords and hype covering the fact that it was actually three application specialists in a trench coat trying to pass themselves off as machine intelligence.

mikkelam 5y ago

Offtopic: More and more I'm noticing the ever-increasing amount of chinese names in machine learning papers. Can anyone explain that? Are these researchers mainly choosing this field out of their own interest, or is China somehow pushing/sponsoring a lot researchers to pursue this field? I realise that China is pursusing AI dominance, is that just what we're seeing? Note this is purely curiosity, I have no problems with the Chinese people (CCP is a different story).

bawolff 5y ago
I don't think there is any big mystery. AI is a hot area, and a reasonable percentage of smart people have Chinese ancestry (regardless of where they reside now).
captainmuon 5y ago
Well first of all, China is a huge country with four times the population of the US or three times of the EU. The greatest "trick" that they have is that they count as one country. It would look different if they were a continent made up of a dozen (still pretty big) countries. I used to work at a Chinese research institute (in a non ML-field) and the team leader took some time and frequently encouraged his students to learn ML, to try to apply it to their work or just for the sake of it. He was very open about the fact that it would benefit the nation, but that also ML researchers were in good demand and it would be great for the student's individual careers. So yeah it is mainly just a very popular topic over there, too. But in addition, I feel that China (the gov/party/research community, whoever) thinks more strategically than for example the EU.
Jyaif 5y ago
You should see it more as China finally starting to pull its weight in scientific research. Their population is 4x that of the US, yet their scientific impact is AFAICT less than that of the US.
cracrecry 5y ago
>Can anyone explain that? It is very simple to understand: There are more than 1.200 million Chinese people just in China, and a couple hundred million more outside. AI is also growing and creating new opportunities, most of them work in Universities on the West. The same happens in other fields like piano playing, there are so many exceptional players because there are so many Chinese.

londons_explore 5y ago

This paper looks more written for investors than as a scientific process for others to work from...

MeteorMarc 5y ago
Yes, I do not know how to feel about this. On the one hand it is great that they found this and made the results public before trying any followup microbiological research. On the other hand, they seem to say: we, engineers, did in two years what you, scientist, failed in doing the last 20 years. They could have just written a clean scientific paper and mention AlphaFold in their methodology section.

didntreadarticl 5y ago

It would be great it we could find a way to use Alphafold to kindof reverse engineer proteins - like to specify the shape you want and then run Alphafold 'backwards' so that you're going from shape -> DNA instead of DNA -> shape

bayesian_horse 5y ago
They mention a framework for protein design. But I guess you'd start out with a know sequence/structure and mutate it towards what you want.
- gilleain 5y ago
  Yes, a common way to design new structures is to 'thread' the sequence onto existing folds. Rarely, there have been attempts to engineer entirely new folds, although it's not clear how necessary that is.
  - gusennan 5y ago
    Early evidence seems to show that AlphaFold has trouble with single point mutations that change a protein's shape, so completely de-novo proteins will be a challenge: https://www.nature.com/articles/s41594-021-00714-2 https://www.nature.com/articles/s41594-021-00714-2
    - dekhn 5y ago
      I would not expect any program to reliably predict the effects of single mutations that massively change the protein's shape unless there was enough high quality structural data and sequence data for both substates and enough signal to predict which substate the protein would adopt after mutation. Fortunately, evolution already encoded robustness against this sort of problem into proteins and the vast majority of single point mutations are tolerated (the resulting enzymes are often nearly as active and stable as the originals).
      - 5y ago

civilized 5y ago

Very cool. It's hard to read the tea leaves of the author affiliation list, but it sure looks like this work was led by a Hong Kong-based startup founded by Russians, with limited assistance from a couple North America-based researchers. I'm curious why those Russians may have chosen to found in HK rather than, say, the Bay Area. We may want to contemplate what this means for America's competitive stance (not that I have any problem with the rest of the world doing awesome cutting edge research).

londons_explore 5y ago
It's pretty hard for Russians to get US visas for startups. They probably went to HK because that's a far easier place to build a team.
- civilized 5y ago
  Russians in particular?
  - londons_explore 5y ago
    I don't know if you've heard, but Russia and the USA haven't been best buddies for quite some time...
    - lajamerr 5y ago
      Have they ever?
      - hangonhn 5y ago
        Yes! During the days of the Russian empire, especially during the Civil War. They were staunch supporters of the Union although their own position on their serfs made that support a bit awkward.
    - civilized 5y ago
      Neither are US and China, but we still have tons of Chinese immigrants powering our tech economy. Maybe they're coming more through student visas, which may be of less interest to the Russians.

cing 5y ago

I agree with the sentiment of this paper (AF can enable drug discovery), but in this specific instance, the authors had a real opportunity contribute a general finding to the scientific community but instead they put in the lowest amount of effort (to a point where they're almost saying nothing at all). The target had dozens of related structures in the protein databank, including relatives with ~40% sequence identity. This target family has a very similar structure, and conserved active site residues. It's relevant that this target has approved cross-CDK family inhibitors (and thousands of data points of CDK family binders on ChEMBL). The conventional way to enable structure-based design is to build a homology model using a similar structure (see here: https://swissmodel.expasy.org/repository/uniprot/Q8IZL9?template=1v0o.1.A&range=3-290 https://swissmodel.expasy.org/repository/uniprot/Q8IZL9?temp...), and in this case, there is very low deviation from the AF2 model and this "old fashioned" approach. To recap, this target had a decent model that would have likely sufficed for drug discovery. The community already knows that "homology models" can be used for structure-based drug design, so any methodological hypotheses of this paper are not supported by evidence.

SubiculumCode 5y ago
And this, dear HN community, is the difference between an expert reading a paper pertaining to their field and the casual reader, or even scientists in unrelated fields reading this paper. I am just not equipped to judge the quality of research in the field.
dekhn 5y ago
Although I agree the authors could have done homology modelling, in this case AlphaFold is already doing that. It knows all the related sequences (through the sequence database similarity graph that it embeds) and has a very sophisticated modelling system. In my guess (I'd have to check with my old friends to be sure) it does as well as if not better in producing atomic accuracy for structural predictions for homology modellers better than a typical modeller could produce. This paper is mainly a flag planted so they can claim they landed on mars first and fastest.
jeejayisbusy 5y ago
another important point to notice is affinity. While 8 uM looks impressive, it is not that hard to develop such potency since compounds are likely to aim ATP binding pocket. It is big, deep and offers many hydrogen bond donors in hindge region. What important for such compounds is selectivity, since you want to inhibit only specific kinase, not all of them. For me it looks like advertising of their platform, not actual scientific achievement.