7 ms·

I am a structural biologist. This is one of the handful of topics that overlaps with my field here. I'm very excited to play with this, although it might eventu

I am a structural biologist. This is one of the handful of topics that overlaps with my field here. I'm very excited to play with this, although it might eventually put me out of a job.

rllearneratwork 5y ago
why would it put you out of job? Wouldn't it just become one of the tools you use?
- dekhn 5y ago
  It would both become a tool he used (to produce initial structures to fit in density maps) and a tool that used his or her output (because alphafold requires known protein structures that are homologous to the one you're predicting).
- nikhilsimha 5y ago
  The implicit assumption you are making is that the demand increases in lock step with productivity gains. 100x faster drug discovery, 100x more drugs need to be discovered => same number of people employed. These correlations do hold for technical fields, but logically there should be a point beyond which productivity gains outpace, demand growth / demand could even stop growing. One should either retool to solve a newer problem before this point is reached, or hope that the point is not reached in the span of their career. Oil rig builders for example - manufacturing has been increasingly automated, but the demand for oil rig building has grown consistently. But they should probably look into solving other problems given that demand is shifting.
  - sbierwagen 5y ago
    >but logically there should be a point beyond which productivity gains outpace The limiting factor on drug approval is clinical trials. Once every living person is enrolled in a clinical trial, we will have hit the maximum rate at which humanity can produce new drugs. That might be more than 10x the current rate, but probably less than 1000x.
    - dekhn 5y ago
      In principle you could put people into multiple trials and gain somewhat additional throughput. Google implemented putting users into multiple different experiments (paper by Tang et al) and that made a huge difference.
    - perl4ever 5y ago
      The number of people needed for a trial depends on the size of the effect. I think we have a warped idea of that, because of the practice of looking for a barely "statistically significant" effect from a substance that isn't really understood. If you have something that just eradicates a disease immediately, because you really understand what you are doing, you don't need very many tests to confirm it works.
  - mensetmanusman 5y ago
    However, complexity for the structures is essentially unbounded on a time scale of the universe timeframe.
  - gopalv 5y ago
    > The implicit assumption you are making is that the demand increases in lock step with productivity gains. This is basically the theory around modern industrial revolution - https://en.wikipedia.org/wiki/Jevons_paradox https://en.wikipedia.org/wiki/Jevons_paradox Efficiency increases demand more than the efficiency saves. You could argue that labour does not follow that, but it is more about the technology improving rather than disrupting.
    - perl4ever 5y ago
      >Efficiency increases demand more than the efficiency saves. That's not some sort of natural law. People latched on to this hypothetical situation and keep elevating its universality. It's just a thing that can happen and may or may not in a given instance.
- COGlory 5y ago
  Right now I make my living cloning, expressing, and purifying proteins, crystallizing them or freezing them onto EM grids, and solving their structure. From start to finish, it's months to years of work for each structure.
AnimalMuppet 5y ago
Here's where I think we need to be going: You go to a doctor's office, sick. 1) They take a blood sample. 2) They find the malignant bacteria and DNA sequence it. 3) If it's a known strain, they know what antibiotics to use on it. 4) If not, they solve protein folding on the genes. 5) From that, they see which existing antibiotics would kill it. 6) If none will, then given the proteins, they have to derive a new antibiotic. 1) is easy. 2) might not be - there can be a lot of things in a blood sample, and finding only the interesting (bad) things might not be simple. The sequencing part is pretty much solved. 3) would take a bit of work, but I think it's possible now. 4) we're getting there. 5) might have a fair amount in common with 3), but it probably takes some additional work. 6) is... probably non-trivial. That's just one research agenda. There are others. You may have to move to related work, but I doubt you're going to be out of a job in this lifetime.
- dzhiurgis 5y ago
  Re step 1 and 2 - here's interesting podcast on how they detect rare infections: https://www.youtube.com/watch?v=MzzD2F73iGU https://www.youtube.com/watch?v=MzzD2F73iGU Basically sequence everything what's in your blood and look for what doesn't match your genome === infection. The problem is this is orders of magnitude more compute intense than whole genome sequencing. Basically increased demand for sequencing far outmatches available compute!
- unholiness 5y ago
  > The sequencing part is pretty much solved. DNA sequencing is still slow and very expensive. On the scales you're talking about it's just not worth it. I think I agree at a high-level that there is a huge reservoir of demand for this technology. But it's also possible that solving protein folding and similar research will simply cease to be a bottleneck for that demand, and people will be out of jobs.

I am a structural biologist. This is one of the handful of topics that overlaps with my field here. I'm very excited to play with this, although it might eventually put me out of a job.

rllearneratwork 5y ago

why would it put you out of job? Wouldn't it just become one of the tools you use?

dekhn 5y ago
It would both become a tool he used (to produce initial structures to fit in density maps) and a tool that used his or her output (because alphafold requires known protein structures that are homologous to the one you're predicting).
nikhilsimha 5y ago
The implicit assumption you are making is that the demand increases in lock step with productivity gains. 100x faster drug discovery, 100x more drugs need to be discovered => same number of people employed. These correlations do hold for technical fields, but logically there should be a point beyond which productivity gains outpace, demand growth / demand could even stop growing. One should either retool to solve a newer problem before this point is reached, or hope that the point is not reached in the span of their career. Oil rig builders for example - manufacturing has been increasingly automated, but the demand for oil rig building has grown consistently. But they should probably look into solving other problems given that demand is shifting.
- sbierwagen 5y ago
  >but logically there should be a point beyond which productivity gains outpace The limiting factor on drug approval is clinical trials. Once every living person is enrolled in a clinical trial, we will have hit the maximum rate at which humanity can produce new drugs. That might be more than 10x the current rate, but probably less than 1000x.
  - dekhn 5y ago
    In principle you could put people into multiple trials and gain somewhat additional throughput. Google implemented putting users into multiple different experiments (paper by Tang et al) and that made a huge difference.
  - perl4ever 5y ago
    The number of people needed for a trial depends on the size of the effect. I think we have a warped idea of that, because of the practice of looking for a barely "statistically significant" effect from a substance that isn't really understood. If you have something that just eradicates a disease immediately, because you really understand what you are doing, you don't need very many tests to confirm it works.
- mensetmanusman 5y ago
  However, complexity for the structures is essentially unbounded on a time scale of the universe timeframe.
- gopalv 5y ago
  > The implicit assumption you are making is that the demand increases in lock step with productivity gains. This is basically the theory around modern industrial revolution - https://en.wikipedia.org/wiki/Jevons_paradox https://en.wikipedia.org/wiki/Jevons_paradox Efficiency increases demand more than the efficiency saves. You could argue that labour does not follow that, but it is more about the technology improving rather than disrupting.
  - perl4ever 5y ago
    >Efficiency increases demand more than the efficiency saves. That's not some sort of natural law. People latched on to this hypothetical situation and keep elevating its universality. It's just a thing that can happen and may or may not in a given instance.
COGlory 5y ago
Right now I make my living cloning, expressing, and purifying proteins, crystallizing them or freezing them onto EM grids, and solving their structure. From start to finish, it's months to years of work for each structure.

AnimalMuppet 5y ago

Here's where I think we need to be going: You go to a doctor's office, sick. 1) They take a blood sample. 2) They find the malignant bacteria and DNA sequence it. 3) If it's a known strain, they know what antibiotics to use on it. 4) If not, they solve protein folding on the genes. 5) From that, they see which existing antibiotics would kill it. 6) If none will, then given the proteins, they have to derive a new antibiotic. 1) is easy. 2) might not be - there can be a lot of things in a blood sample, and finding only the interesting (bad) things might not be simple. The sequencing part is pretty much solved. 3) would take a bit of work, but I think it's possible now. 4) we're getting there. 5) might have a fair amount in common with 3), but it probably takes some additional work. 6) is... probably non-trivial. That's just one research agenda. There are others. You may have to move to related work, but I doubt you're going to be out of a job in this lifetime.

dzhiurgis 5y ago
Re step 1 and 2 - here's interesting podcast on how they detect rare infections: https://www.youtube.com/watch?v=MzzD2F73iGU https://www.youtube.com/watch?v=MzzD2F73iGU Basically sequence everything what's in your blood and look for what doesn't match your genome === infection. The problem is this is orders of magnitude more compute intense than whole genome sequencing. Basically increased demand for sequencing far outmatches available compute!
unholiness 5y ago
> The sequencing part is pretty much solved. DNA sequencing is still slow and very expensive. On the scales you're talking about it's just not worth it. I think I agree at a high-level that there is a huge reservoir of demand for this technology. But it's also possible that solving protein folding and similar research will simply cease to be a bottleneck for that demand, and people will be out of jobs.