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I don't think identification of possible new materials is a rate-limiting step for discovery of better catalysts, batteries, etc. The problem is not coming up w

I don't think identification of possible new materials is a rate-limiting step for discovery of better catalysts, batteries, etc. The problem is not coming up with new materials -- it's coming up with new materials that _have desired properties_ and _can be cheaply synthesized_. It's like if you asked a chemist to draw a few possible structures for organic molecules that have never been synthesized. They can do that. But not all of those possible molecules they came up with will be easy to synthesize. And neither they nor anyone else (without doing a lot of experimental work) will be able to tell you which of those possible structures, if any, would work as a painkiller or an oncology drug. Still, I do think this is a nice demonstration of how more data enables very accurate predictions of energies that would otherwise require expensive DFT calculations. That part is definitely interesting.

aabhay 3y ago
100% agreed. This is primarily a breakthrough on using graph networks to show some promise on the task. It will take several more iterations for it to be transformative to the industry.
CuriousCosmic 3y ago
I think a more interesting application of this process is to attempt to find easier, safer, more reliable, or more efficient methods of producing or processing existing materials. See: the more or less accidental rediscovery of room temperature polyester/PET recycling (including separation from blended fabrics without damaging the cotton) using CO2 as a catalyst. There exist quite a few cases of very simple solutions to very difficult problems where the start and end products are already known, but we just don't know how to effectively get from A to B without causing certain undesirable side-effects.
dekhn 3y ago
I'm certain you could build an embedding that provided a utility function for molecules based on price and synthesizability. That's an approximation of what the chemist's brain is doing. You wouldn't ask a chemist to evaluate the molecules (in drug discovery), though- you'd have a molecular biologist (really a lab tech) set up a screening campaign, and in many cases, the biological readout that predicts something could work as a painkiller or oncology drug is relatively straightforward to implement experimentally at scale (high throughput screening). Unfortunately those readouts aren't super-predictive of the full biology, however. I expect DeepMind or Isomorphic to announce, in the next five years, that they have made a model that can quickly identify whether a specific molecule would be likely to pass clinical trials and the rest of the FDA process. With a false negative rate ("predict that a drug would not get through to approval, but in reality it would have") below around 25%, we could easily save billions a year in failed drug costs.
- bglazer 3y ago
  I would be pretty surprised if Deepmind could automatically identify drugs that pass phase 3 trials in the next five years. First, there’s a banal point that many trials take years to read out, so any prospective study would have to be beginning about now. I don’t think Deepmind or anyone else can do what you describe currently. More importantly we just don’t understand human biology very well at all. Like there are phenomena that are critically important to drug and disease behavior that are just totally unknown. So machine learning systems trained on current knowledge just won’t have the necessary data. But I’ve been very surprised before by ML advances so who knows?

aabhay 3y ago

100% agreed. This is primarily a breakthrough on using graph networks to show some promise on the task. It will take several more iterations for it to be transformative to the industry.

CuriousCosmic 3y ago

I think a more interesting application of this process is to attempt to find easier, safer, more reliable, or more efficient methods of producing or processing existing materials. See: the more or less accidental rediscovery of room temperature polyester/PET recycling (including separation from blended fabrics without damaging the cotton) using CO2 as a catalyst. There exist quite a few cases of very simple solutions to very difficult problems where the start and end products are already known, but we just don't know how to effectively get from A to B without causing certain undesirable side-effects.

dekhn 3y ago

I'm certain you could build an embedding that provided a utility function for molecules based on price and synthesizability. That's an approximation of what the chemist's brain is doing. You wouldn't ask a chemist to evaluate the molecules (in drug discovery), though- you'd have a molecular biologist (really a lab tech) set up a screening campaign, and in many cases, the biological readout that predicts something could work as a painkiller or oncology drug is relatively straightforward to implement experimentally at scale (high throughput screening). Unfortunately those readouts aren't super-predictive of the full biology, however. I expect DeepMind or Isomorphic to announce, in the next five years, that they have made a model that can quickly identify whether a specific molecule would be likely to pass clinical trials and the rest of the FDA process. With a false negative rate ("predict that a drug would not get through to approval, but in reality it would have") below around 25%, we could easily save billions a year in failed drug costs.

bglazer 3y ago
I would be pretty surprised if Deepmind could automatically identify drugs that pass phase 3 trials in the next five years. First, there’s a banal point that many trials take years to read out, so any prospective study would have to be beginning about now. I don’t think Deepmind or anyone else can do what you describe currently. More importantly we just don’t understand human biology very well at all. Like there are phenomena that are critically important to drug and disease behavior that are just totally unknown. So machine learning systems trained on current knowledge just won’t have the necessary data. But I’ve been very surprised before by ML advances so who knows?