7 ms·

AI is changing chemical discovery

125 points by andreyk 5y ago

fock 5y ago
I don't know if I have missed the big thing here (was supposed to do exactly the same flowery thing described there for crystals around 2019-2020), but the graphic with the Autoencoder is roughly what people did in 2018 (Gomez-Bombarelli, https://pubs.acs.org/doi/10.1021/acscentsci.7b00572 https://pubs.acs.org/doi/10.1021/acscentsci.7b00572), I think the review cited reproduced this. Also notice: it has the MLP in the middle, the performance of which was/is not really helping either - especially if your model should actually produce novel stuff, e.g. extrapolate. Finally: every kid can draw up novel structures. Then: how do you actually fabricate these (in the case of real novel chemistry and not some building-block stuff). Noone has a clue! I for myself have decided that for now (with the data at hand and non-Alphafold-budgets) the 2 keys areas, where you can actually help computational chemistry are: - creating really robust and generally applicable ML-MD-potentials, potentially using graphs https://arxiv.org/abs/2106.08903 https://arxiv.org/abs/2106.08903 (or a traditional approach: https://www.nature.com/articles/s41467-020-20427-2 https://www.nature.com/articles/s41467-020-20427-2). Facebook is also working in this area: https://pubs.acs.org/doi/10.1021/acscatal.0c04525 https://pubs.acs.org/doi/10.1021/acscatal.0c04525 - and approximating exchange correlation functionals (... Google and some guys at Oxford, which got stomped over by the deepmind-PR machine https://arxiv.org/pdf/2102.04229.pdf https://arxiv.org/pdf/2102.04229.pdf): https://www.science.org/doi/10.1126/science.abj6511 https://www.science.org/doi/10.1126/science.abj6511 If anyone can tell me how those generative models spit out graphs which look like reality (actually this is imho part of AlphaFold), wake me up.
- busyant 5y ago
  > every kid can draw up novel structures. Then: how do you actually fabricate these (in the case of real novel chemistry and not some building-block stuff). Noone has a clue! Yep. I worked at a biotech startup in the early/mid 2000s. We had a 2-pronged approach to finding small molecule drugs: 1) traditional medicinal chemistry based on simple SAR (structure-activity relationships) and 2) predictive modeling (before ML was hot). The traditional med chemists were, in my opinion, rightfully skeptical of the suggestions coming out of the predictive modeling group ("That's a great suggestion, but can you tell me how to synthesize it?"). As one of my co-workers said to me: "The predictions made by the modeling group range from pretty bad to ... completely worthless." It's possible that things have gotten better, though, as I haven't done that type of work since about 2008.
  - csdvrx 5y ago
    > The traditional med chemists were, in my opinion, rightfully skeptical of the suggestions coming out of the predictive modeling group ("That's a great suggestion, but can you tell me how to synthesize it?"). Start by plugging it into askcos.mit.edu/retro/ then, do your job? > As one of my co-workers said to me: "The predictions made by the modeling group range from pretty bad to ... completely worthless." Workers feeling threatened by technology think the technology is bad or worthless, news at 11.
    - aemoron 5y ago
      Chemical synthesis is much more than just retrosynthesis. Even if it was, you can not just plug in the novel previously unsynthesized molecule and expect any reasonable results. Furthermore, the tool is based on Reaxys which in turn uses already established reaction routes and conditions from literature and patents. Good luck optimizing yields for something you don't know even how to synthesize, let alone what conditions to use.
      - csdvrx 5y ago
        > Chemical synthesis is much more than just retrosynthesis. Of course, but we are talking about chemical discovery - after which you want to test if the compound theoretical capabilities work on cells. Yields are not yet a concern! > expect any reasonable results No it won't do all the work, but it will direction, and suggestion for which pathways could be used.
    - busyant 5y ago
      > Workers feeling threatened by technology think the technology is bad or worthless, news at 11. I appreciate the sentiment and I think it's understandable to think that. In this particular case, however, my co-worker was one of the smartest / most talented people I've worked with. I can assure you that he did not feel threatened in any way. His comment was sardonic, but not borne of insecurity. To be fair, the members of the modeling group were also quite talented. They were largely derived from one of the more famous physical/chemical modeling groups at one of the HYPS schools. But even they acknowledged that on a good day, the best they could do was offer suggestions / ideas to the medicinal chemists. In fact, one of the members of the modeling group said this to me once (paraphrasing): The medicinal chemists are the high-priests of drug discovery. We can help, but they run the show. As mentioned by someone who responded to my original comment, the usefulness of ML/modeling has likely gotten much better over the past 10 - 15 years.
- gpcr1949 5y ago
  > Finally: every kid can draw up novel structures. Then: how do you actually fabricate these (in the case of real novel chemistry and not some building-block stuff). Noone has a clue! I personally have a clue, and the entire field of organic chemistry has a clue, given enough time and money most reasonable structures can be synthesized (and QED+SAScore+etc and then human filter is often enough to weed out the problem compounds that will be unstable or hard to make). Actually even some of the state of the art synthesis prediction models are able to predict decent routes if the compounds are relatively simple [0]. The issue is that in silico activity/property prediction is often not reliable enough for the effort to design and execute a synthesis to be worth it, especially because as typically the molecules will get more dissimilar to known compounds with the given activity, the predictions will also often become less reliable. In the end, what would happen is that you just spend 3 months of your master student's time on a pharmacological dead end. Conversely, some of the "novel predictions" of ML pipelines includign de novo structure generation can be very close to known molecules, which makes the measured activity to be somewhat of a triviality.[1] For this reason, it makes sense to spend the budget on building block-based "make on demand" structures that will have 90% fulfillment, that will take 1-2 months from placed order to compound in hand and that will be significantly cheaper per compound, because you can iterate faster. Recent work around large scale docking has shown that this approach seems to work decently for well behaved systems.[2] On the other hand, some truly novel frameworks are not available via the building block approach, which can also be important for IP. More fundamentally, of course you are correct, and I agree with you: having a lot of structures is in itself not that useful. Getting closer to physically more meaningful and fundamental processes and speeding them up to the extent possible can generate way more transparent reliable activity and novelty. [0] https://www.sciencedirect.com/science/article/pii/S2451929418300639 https://www.sciencedirect.com/science/article/pii/S245192941... [1] http://www.drugdiscovery.net/2019/09/03/so-did-ai-just-discover-its-first-drug-comment-on-deep-learning-enables-rapid-identification-of-potent-ddr1-kinase-inhibitors/ http://www.drugdiscovery.net/2019/09/03/so-did-ai-just-disco... [2] https://www.nature.com/articles/s41586-021-04175-x.pdf https://www.nature.com/articles/s41586-021-04175-x.pdf
  - entee 5y ago
    There's a lot that can be learned with building-block based experiments. If you do a building block based experiment then train a model, then predict new compounds, the models do generalize meaningfully outside the original set of building blocks into other sets of building blocks (including variations on different ways of linking the building blocks). Granted that's not the "fully novel scaffold" test, however it suggests that there should be some positive predictive value on novel scaffolds. We've done work in this area and will be publishing some results later in the year.
uejfiweun 5y ago
Don't mean to sound cynical here, but is AI actually gonna change chemical discovery? Or is AI gonna "change chemical discovery" in the same way that it will "make radiologists obsolete", or "revolutionize healthcare with Watson", or "put millions of truckers out of business"? There's certainly a lot of marketing, technical talent, and hope behind modern "AI", but I'm not really aware of any major part of our economy that's been THAT changed by it.
- shoguning 5y ago
  Advertising and media? Robots bid on the ads that billions see every day, and decide what videos billions of people watch.
  - oaktrout 5y ago
    It seems like AI is good at finding what's popular (video views/ad clicks), but it doesn't seem to be very good at science/engineering (drug discovery/self driving cars/radiology).
    - eru 5y ago
      This reminds me of Moravec's paradox (https://en.wikipedia.org/wiki/Moravec%27s_paradox https://en.wikipedia.org/wiki/Moravec%27s_paradox). Basically, tasks that seem hard for humans are easy for computers and vice versa. Have a look at how driving a taxi has changed. (And I include the likes of Uber here.) The hard part for humans used to be knowing all the roads in a city and selecting the best route quickly and reliably. Almost any adult can do the actual second-to-second driving reasonably competently in almost any city on the planet. Now, we have outsourced the 'hard' part to Google Maps. But we are still far from a machine that drives in arbitrary locations on the globe. As far as I know, Waymo has the most mature system currently in development, but requires absurd amounts of precise mapping data for any location they want to drive in. And let's not even talk about the even more 'trivial' task of chatting to the passengers. > [...], but it doesn't seem to be very good at science/engineering (drug discovery/self driving cars/radiology). Technology has already automated huge chunks for science and engineering. We just don't call any of the already solved chunks by the name of AI anymore.
mrkramer 5y ago
Isn't AI chemical discovery essentially machine brute forcing solutions to see which one fits/works? I think more attention needs to be put into designing better software solutions then merely putting more computational power into problem solving.
- mariebks 5y ago
  You could say that it’s optimized brute forcing. It seems to me that machine learning applied to combinatorial search problems of this nature cut down the search space massively by recognizing patterns of combinations that have a high probability of being good, and then traversing those paths, similar to AlphaGo. This is a completely naive take, I’d like to hear other thoughts on this.
- hardwaresofton 5y ago
  Could you explain what advantages there are to having the software work better as opposed to just throwing more computational power at it? I know this doesn't necessarily apply, but if the solution space for certain niche problems is so small that we can just drown the problem in compute, I couldn't care less that the algo was N^2 or whatever, or that the UI was less than ideal. Maybe I'm not thinking deeply enough about what you mean by "better software solutions". If I run a batch job and it spits out 10,000 compounds that I can try to a certain affect, then it then becomes a filtering problem where I can apply humans and do more traditional science, and if it was feasible to just try everything in parallel that option is nice as well, feels like how you got to that 10,000 compounds doesn't matter much. Looking forward to hearing just how wrong my simplistic view is.
  - hashimotonomora 5y ago
    Computer power grows ~N but molecule complexity grows something like ~N!
    - hardwaresofton 5y ago
      Ah yeah so I was guessing here that we're talking about a subset that is solvable with the current methods -- as in within reach enough to be solvable by relatively naive methods
praccu 5y ago
Shameless self promotion: I wrote one of the more cited papers in the field [0], back in 2016. A key challenge: very few labs have enough data. Something I view as a key insight: a lot of labs are doing absurdly labor intensive exploratory synthesis without clear hypotheses guiding their work. One of our more useful tasks turned out to be interactively helping scientists refine their experiments before running them. Another was helping scientists develop hypotheses for _why_ reactions were occuring, because they hadn't been able to build principled models that predicted which properties were predictive of reaction formation. Going all the way to synthesis is nice, but there's a lot of lower hanging fruit involved in making scientists more effective. [0] https://www.nature.com/articles/nature17439 https://www.nature.com/articles/nature17439
- kilroy123 5y ago
  I'm trying to get a startup off the ground that tackles this. Would love to chat more with you about this.
  - malux85 5y ago
    Me too, also tech nomad. I'll email you
- czbond 5y ago
  Question: How are labs doing the exploratory work without a clear hypothesis? Are they essentially doing some version of brute force?
  - hashimotonomora 5y ago
    Experienced chemists can look at molecule diagrams and have an intuition as to its activity and similarity to other known molecules. It’s like most of science and math: most discoveries begin with intuition and are demonstrated rigorously afterwards. I believe Poincare said something to this end.
    - amelius 5y ago
watersb 5y ago
Wild. I was first introduced to heuristic search in drug discovery at a biotech company in 1993. Support vector networks were searching for good shapes by 1997, maybe before then, but the vocabulary we used to describe the search technique was different. A huge computational hurdle was protein folding. We had brute-force searches for plausible shapes, lots of supervision by the chemists, weeks per iteration on the best workstations we could get. $250,000+ SGI Onyx, then DEC sent over an Alpha workstation. It's come a long ways.
tus666 5y ago
> Chemistry is what we turn to when we are looking for a new superconductor, a vaccine I am not sure that is very true at all.
sdk16420 5y ago
A similar thing is happening in inorganic chemistry. On https://materialsproject.org https://materialsproject.org probably the majority of materials are 'discovered' computationally, by using DFT simulations to determine if and how stable any crystal structure would be. Using this database, AI can be used to find suitable materials for any application. [1] [1] https://scholar.google.com/scholar?q=materials%20project%20high%20throughput https://scholar.google.com/scholar?q=materials%20project%20h...
graderjs 5y ago
To give the reader some perspective, it has been estimated that the pharmacologically active chemical space (i.e. the number of molecules) is 1060! Not sure if they mean 1060 factorial or 10 to the 60.
dbodin11 5y ago
[dead]
Mizza 5y ago
I have an AI chemistry project that appears to work on my test data, but I had to put it on the backburner because I simply can't (couldn't) find a 3070/80 anywhere! I stopped looking 6 months ago, does anybody know a reliable place where I can snag one?
- umvi 5y ago
  Have you considered renting cloud GPUs by the minute? You can rent some pretty powerful GPUs from cloud providers (i.e. https://cloud.google.com/compute/docs/gpus https://cloud.google.com/compute/docs/gpus)
  - eru 5y ago
    If you are really on a tight budget, but have time to fiddle, you can try to make something work on top of Google's colab, too.
- tehsauce 5y ago
  vast.ai, nothing else is remotely as cheap
  - kukx 5y ago
    The price is great, the downside is that you do not know whether the server owner reads your data. The "jobs" run in a Docker container on somebody's machine.
    - ShamelessC 5y ago
      Yeah. Use fresh ssh keys, and don't train with sensitive data. If that's an option for you, vast is great.
- munchbunny 5y ago
  If you're willing to pay scalper prices, there's a relatively consistent availability. If you're looking for closer to MSRP, they're in short supply and your choices are either waiting lists or racing other people for online restocks. I got a 3080 a month ago only after waiting on EVGA's wait list for just over a year. Why do you need a 3070/3080 specifically? If it's to run something like Tensorflow or CUDA code more generally, could you do it with an older card, or the more available 3060s?

125 points by andreyk 5y ago

fock 5y ago

I don't know if I have missed the big thing here (was supposed to do exactly the same flowery thing described there for crystals around 2019-2020), but the graphic with the Autoencoder is roughly what people did in 2018 (Gomez-Bombarelli, https://pubs.acs.org/doi/10.1021/acscentsci.7b00572 https://pubs.acs.org/doi/10.1021/acscentsci.7b00572), I think the review cited reproduced this. Also notice: it has the MLP in the middle, the performance of which was/is not really helping either - especially if your model should actually produce novel stuff, e.g. extrapolate. Finally: every kid can draw up novel structures. Then: how do you actually fabricate these (in the case of real novel chemistry and not some building-block stuff). Noone has a clue! I for myself have decided that for now (with the data at hand and non-Alphafold-budgets) the 2 keys areas, where you can actually help computational chemistry are: - creating really robust and generally applicable ML-MD-potentials, potentially using graphs https://arxiv.org/abs/2106.08903 https://arxiv.org/abs/2106.08903 (or a traditional approach: https://www.nature.com/articles/s41467-020-20427-2 https://www.nature.com/articles/s41467-020-20427-2). Facebook is also working in this area: https://pubs.acs.org/doi/10.1021/acscatal.0c04525 https://pubs.acs.org/doi/10.1021/acscatal.0c04525 - and approximating exchange correlation functionals (... Google and some guys at Oxford, which got stomped over by the deepmind-PR machine https://arxiv.org/pdf/2102.04229.pdf https://arxiv.org/pdf/2102.04229.pdf): https://www.science.org/doi/10.1126/science.abj6511 https://www.science.org/doi/10.1126/science.abj6511 If anyone can tell me how those generative models spit out graphs which look like reality (actually this is imho part of AlphaFold), wake me up.

busyant 5y ago
> every kid can draw up novel structures. Then: how do you actually fabricate these (in the case of real novel chemistry and not some building-block stuff). Noone has a clue! Yep. I worked at a biotech startup in the early/mid 2000s. We had a 2-pronged approach to finding small molecule drugs: 1) traditional medicinal chemistry based on simple SAR (structure-activity relationships) and 2) predictive modeling (before ML was hot). The traditional med chemists were, in my opinion, rightfully skeptical of the suggestions coming out of the predictive modeling group ("That's a great suggestion, but can you tell me how to synthesize it?"). As one of my co-workers said to me: "The predictions made by the modeling group range from pretty bad to ... completely worthless." It's possible that things have gotten better, though, as I haven't done that type of work since about 2008.
- csdvrx 5y ago
  > The traditional med chemists were, in my opinion, rightfully skeptical of the suggestions coming out of the predictive modeling group ("That's a great suggestion, but can you tell me how to synthesize it?"). Start by plugging it into askcos.mit.edu/retro/ then, do your job? > As one of my co-workers said to me: "The predictions made by the modeling group range from pretty bad to ... completely worthless." Workers feeling threatened by technology think the technology is bad or worthless, news at 11.
  - aemoron 5y ago
    Chemical synthesis is much more than just retrosynthesis. Even if it was, you can not just plug in the novel previously unsynthesized molecule and expect any reasonable results. Furthermore, the tool is based on Reaxys which in turn uses already established reaction routes and conditions from literature and patents. Good luck optimizing yields for something you don't know even how to synthesize, let alone what conditions to use.
    - csdvrx 5y ago
      > Chemical synthesis is much more than just retrosynthesis. Of course, but we are talking about chemical discovery - after which you want to test if the compound theoretical capabilities work on cells. Yields are not yet a concern! > expect any reasonable results No it won't do all the work, but it will direction, and suggestion for which pathways could be used.
  - busyant 5y ago
    > Workers feeling threatened by technology think the technology is bad or worthless, news at 11. I appreciate the sentiment and I think it's understandable to think that. In this particular case, however, my co-worker was one of the smartest / most talented people I've worked with. I can assure you that he did not feel threatened in any way. His comment was sardonic, but not borne of insecurity. To be fair, the members of the modeling group were also quite talented. They were largely derived from one of the more famous physical/chemical modeling groups at one of the HYPS schools. But even they acknowledged that on a good day, the best they could do was offer suggestions / ideas to the medicinal chemists. In fact, one of the members of the modeling group said this to me once (paraphrasing): The medicinal chemists are the high-priests of drug discovery. We can help, but they run the show. As mentioned by someone who responded to my original comment, the usefulness of ML/modeling has likely gotten much better over the past 10 - 15 years.
gpcr1949 5y ago
> Finally: every kid can draw up novel structures. Then: how do you actually fabricate these (in the case of real novel chemistry and not some building-block stuff). Noone has a clue! I personally have a clue, and the entire field of organic chemistry has a clue, given enough time and money most reasonable structures can be synthesized (and QED+SAScore+etc and then human filter is often enough to weed out the problem compounds that will be unstable or hard to make). Actually even some of the state of the art synthesis prediction models are able to predict decent routes if the compounds are relatively simple [0]. The issue is that in silico activity/property prediction is often not reliable enough for the effort to design and execute a synthesis to be worth it, especially because as typically the molecules will get more dissimilar to known compounds with the given activity, the predictions will also often become less reliable. In the end, what would happen is that you just spend 3 months of your master student's time on a pharmacological dead end. Conversely, some of the "novel predictions" of ML pipelines includign de novo structure generation can be very close to known molecules, which makes the measured activity to be somewhat of a triviality.[1] For this reason, it makes sense to spend the budget on building block-based "make on demand" structures that will have 90% fulfillment, that will take 1-2 months from placed order to compound in hand and that will be significantly cheaper per compound, because you can iterate faster. Recent work around large scale docking has shown that this approach seems to work decently for well behaved systems.[2] On the other hand, some truly novel frameworks are not available via the building block approach, which can also be important for IP. More fundamentally, of course you are correct, and I agree with you: having a lot of structures is in itself not that useful. Getting closer to physically more meaningful and fundamental processes and speeding them up to the extent possible can generate way more transparent reliable activity and novelty. [0] https://www.sciencedirect.com/science/article/pii/S2451929418300639 https://www.sciencedirect.com/science/article/pii/S245192941... [1] http://www.drugdiscovery.net/2019/09/03/so-did-ai-just-discover-its-first-drug-comment-on-deep-learning-enables-rapid-identification-of-potent-ddr1-kinase-inhibitors/ http://www.drugdiscovery.net/2019/09/03/so-did-ai-just-disco... [2] https://www.nature.com/articles/s41586-021-04175-x.pdf https://www.nature.com/articles/s41586-021-04175-x.pdf
- entee 5y ago
  There's a lot that can be learned with building-block based experiments. If you do a building block based experiment then train a model, then predict new compounds, the models do generalize meaningfully outside the original set of building blocks into other sets of building blocks (including variations on different ways of linking the building blocks). Granted that's not the "fully novel scaffold" test, however it suggests that there should be some positive predictive value on novel scaffolds. We've done work in this area and will be publishing some results later in the year.

uejfiweun 5y ago

Don't mean to sound cynical here, but is AI actually gonna change chemical discovery? Or is AI gonna "change chemical discovery" in the same way that it will "make radiologists obsolete", or "revolutionize healthcare with Watson", or "put millions of truckers out of business"? There's certainly a lot of marketing, technical talent, and hope behind modern "AI", but I'm not really aware of any major part of our economy that's been THAT changed by it.

shoguning 5y ago
Advertising and media? Robots bid on the ads that billions see every day, and decide what videos billions of people watch.
- oaktrout 5y ago
  It seems like AI is good at finding what's popular (video views/ad clicks), but it doesn't seem to be very good at science/engineering (drug discovery/self driving cars/radiology).
  - eru 5y ago
    This reminds me of Moravec's paradox (https://en.wikipedia.org/wiki/Moravec%27s_paradox https://en.wikipedia.org/wiki/Moravec%27s_paradox). Basically, tasks that seem hard for humans are easy for computers and vice versa. Have a look at how driving a taxi has changed. (And I include the likes of Uber here.) The hard part for humans used to be knowing all the roads in a city and selecting the best route quickly and reliably. Almost any adult can do the actual second-to-second driving reasonably competently in almost any city on the planet. Now, we have outsourced the 'hard' part to Google Maps. But we are still far from a machine that drives in arbitrary locations on the globe. As far as I know, Waymo has the most mature system currently in development, but requires absurd amounts of precise mapping data for any location they want to drive in. And let's not even talk about the even more 'trivial' task of chatting to the passengers. > [...], but it doesn't seem to be very good at science/engineering (drug discovery/self driving cars/radiology). Technology has already automated huge chunks for science and engineering. We just don't call any of the already solved chunks by the name of AI anymore.

mrkramer 5y ago

Isn't AI chemical discovery essentially machine brute forcing solutions to see which one fits/works? I think more attention needs to be put into designing better software solutions then merely putting more computational power into problem solving.

mariebks 5y ago
You could say that it’s optimized brute forcing. It seems to me that machine learning applied to combinatorial search problems of this nature cut down the search space massively by recognizing patterns of combinations that have a high probability of being good, and then traversing those paths, similar to AlphaGo. This is a completely naive take, I’d like to hear other thoughts on this.
hardwaresofton 5y ago
Could you explain what advantages there are to having the software work better as opposed to just throwing more computational power at it? I know this doesn't necessarily apply, but if the solution space for certain niche problems is so small that we can just drown the problem in compute, I couldn't care less that the algo was N^2 or whatever, or that the UI was less than ideal. Maybe I'm not thinking deeply enough about what you mean by "better software solutions". If I run a batch job and it spits out 10,000 compounds that I can try to a certain affect, then it then becomes a filtering problem where I can apply humans and do more traditional science, and if it was feasible to just try everything in parallel that option is nice as well, feels like how you got to that 10,000 compounds doesn't matter much. Looking forward to hearing just how wrong my simplistic view is.
- hashimotonomora 5y ago
  Computer power grows ~N but molecule complexity grows something like ~N!
  - hardwaresofton 5y ago
    Ah yeah so I was guessing here that we're talking about a subset that is solvable with the current methods -- as in within reach enough to be solvable by relatively naive methods

praccu 5y ago

Shameless self promotion: I wrote one of the more cited papers in the field [0], back in 2016. A key challenge: very few labs have enough data. Something I view as a key insight: a lot of labs are doing absurdly labor intensive exploratory synthesis without clear hypotheses guiding their work. One of our more useful tasks turned out to be interactively helping scientists refine their experiments before running them. Another was helping scientists develop hypotheses for _why_ reactions were occuring, because they hadn't been able to build principled models that predicted which properties were predictive of reaction formation. Going all the way to synthesis is nice, but there's a lot of lower hanging fruit involved in making scientists more effective. [0] https://www.nature.com/articles/nature17439 https://www.nature.com/articles/nature17439

kilroy123 5y ago
I'm trying to get a startup off the ground that tackles this. Would love to chat more with you about this.
- malux85 5y ago
  Me too, also tech nomad. I'll email you
czbond 5y ago
Question: How are labs doing the exploratory work without a clear hypothesis? Are they essentially doing some version of brute force?
- hashimotonomora 5y ago
  Experienced chemists can look at molecule diagrams and have an intuition as to its activity and similarity to other known molecules. It’s like most of science and math: most discoveries begin with intuition and are demonstrated rigorously afterwards. I believe Poincare said something to this end.
  - amelius 5y ago

watersb 5y ago

Wild. I was first introduced to heuristic search in drug discovery at a biotech company in 1993. Support vector networks were searching for good shapes by 1997, maybe before then, but the vocabulary we used to describe the search technique was different. A huge computational hurdle was protein folding. We had brute-force searches for plausible shapes, lots of supervision by the chemists, weeks per iteration on the best workstations we could get. $250,000+ SGI Onyx, then DEC sent over an Alpha workstation. It's come a long ways.

tus666 5y ago

> Chemistry is what we turn to when we are looking for a new superconductor, a vaccine I am not sure that is very true at all.

sdk16420 5y ago

A similar thing is happening in inorganic chemistry. On https://materialsproject.org https://materialsproject.org probably the majority of materials are 'discovered' computationally, by using DFT simulations to determine if and how stable any crystal structure would be. Using this database, AI can be used to find suitable materials for any application. [1] [1] https://scholar.google.com/scholar?q=materials%20project%20high%20throughput https://scholar.google.com/scholar?q=materials%20project%20h...

graderjs 5y ago

To give the reader some perspective, it has been estimated that the pharmacologically active chemical space (i.e. the number of molecules) is 1060! Not sure if they mean 1060 factorial or 10 to the 60.

dbodin11 5y ago

[dead]

Mizza 5y ago

I have an AI chemistry project that appears to work on my test data, but I had to put it on the backburner because I simply can't (couldn't) find a 3070/80 anywhere! I stopped looking 6 months ago, does anybody know a reliable place where I can snag one?

umvi 5y ago
Have you considered renting cloud GPUs by the minute? You can rent some pretty powerful GPUs from cloud providers (i.e. https://cloud.google.com/compute/docs/gpus https://cloud.google.com/compute/docs/gpus)
- eru 5y ago
  If you are really on a tight budget, but have time to fiddle, you can try to make something work on top of Google's colab, too.
tehsauce 5y ago
vast.ai, nothing else is remotely as cheap
- kukx 5y ago
  The price is great, the downside is that you do not know whether the server owner reads your data. The "jobs" run in a Docker container on somebody's machine.
  - ShamelessC 5y ago
    Yeah. Use fresh ssh keys, and don't train with sensitive data. If that's an option for you, vast is great.
munchbunny 5y ago
If you're willing to pay scalper prices, there's a relatively consistent availability. If you're looking for closer to MSRP, they're in short supply and your choices are either waiting lists or racing other people for online restocks. I got a 3080 a month ago only after waiting on EVGA's wait list for just over a year. Why do you need a 3070/3080 specifically? If it's to run something like Tensorflow or CUDA code more generally, could you do it with an older card, or the more available 3060s?