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as a computational chemist I must say end-use might differ significantly from these screening studies, it is more of a statement on current computing capabiliti

as a computational chemist I must say end-use might differ significantly from these screening studies, it is more of a statement on current computing capabilities and a little bit of science icing

dekhn 8y ago
it's not even a statement of current computing capabilities if they used 1 CPU-day on 1,500 machines. That's a pittance.
- deleted 8y ago
  [deleted]
- throwitaway6512 8y ago
  Computing capabilities inside this field. You can throw as much cpu time as you want at a bad model and it won’t mean anything. The value here is that it’s not just bruteforcing every concievable compound but instead goes through a sequence of potentially synthesisable drugs which are interesting according to docking simulations. They could likely simulate tons more, but there is no guarentee that they are; Actually synthesizable, actually hit the target, aren’t toxic. So at some point the current computing capabilites fall shot. But not because we can’t throw more cpu hours at the problem, simply because we don’t have the computational tools to cross those bery important barriers available at all.
  - dekhn 8y ago
    I developed the Exacycle idle computing system at Google and we ran extensive docking, with very good models of synthesizable drugs. Based on my in-depth knowledge of this field, along with extensive experience running a system that produced 1M CPU hours per hour 24/7 and made significant improvements in protein design and structure modelling. Using more CPU time in this would have helped a lot.
- momeara 8y ago
  Co-author here (AMA)--A large-scale docking screen of 116M molecules takes ~1100 cpu days on our cluster, working out to about 1 mol/sec, which is very fast for virtual screening. What this doesn't account for is this requires about 30 minutes per compound to precompute information (conformations, partial charges, etc.). So this works out to ~6M cpu/hours to prepare the library for screening, which is a substantial amount of computation. We're loading about 1M molecules a day and have a 2-3 year backlog of compounds to load from Enamine. The good news is that once the library is prepared, it is quick to screen at more targets--and we make the pre-computed library available at zinc15.docking.org. Interestingly, as the library grows a limiting factor is storing the library on disk. It is now ~20T. We've set up several mirrors around the world for groups that are actively using it. An interesting problem will be to see if preparing compounds for screening on the fly (e.g. with machine learning models) can overcome this limitation to keep up with library growth. A big question for us is what will the return on investment in screening larger and larger libraries be? One of the take aways from this work is if docking has moderate enrichment, than screening larger libraries not only gives more hits but actually can increase the hit-rate for the top scoring compounds.
  - dekhn 8y ago
    I'm curious why 20 terabytes on disk is a challenge. That sounds like a bucket in S3 problem
  - cing 8y ago
    I know that docking using GPU is about an order of magnitude faster than CPU (see today's Schrodinger 2019-1 release notes, https://youtu.be/K4AYdBvuOe4?t=90 https://youtu.be/K4AYdBvuOe4?t=90). Is there a way of doing GPU accelerated precomputation though?
    - momeara 8y ago
      Hey Chris--We're right now using a mix of commercial and open source software like Omega, Corina, AMSOL, and Mol2DB. Probably the slowest step is generating the partial charges for each conformer with a reasonably high quality semi-empirical forcefield. I'm not sure if there are competitive (in terms of quality) GPU based methods, but if there were methods that were ~1000 times faster as can be the case for GPU based methods, it would definitely speed up the pre-computation or make on-the-fly prep feasible. Do you have any ideas of where we should look?

as a computational chemist I must say end-use might differ significantly from these screening studies, it is more of a statement on current computing capabilities and a little bit of science icing

dekhn 8y ago

it's not even a statement of current computing capabilities if they used 1 CPU-day on 1,500 machines. That's a pittance.

deleted 8y ago
[deleted]
throwitaway6512 8y ago
Computing capabilities inside this field. You can throw as much cpu time as you want at a bad model and it won’t mean anything. The value here is that it’s not just bruteforcing every concievable compound but instead goes through a sequence of potentially synthesisable drugs which are interesting according to docking simulations. They could likely simulate tons more, but there is no guarentee that they are; Actually synthesizable, actually hit the target, aren’t toxic. So at some point the current computing capabilites fall shot. But not because we can’t throw more cpu hours at the problem, simply because we don’t have the computational tools to cross those bery important barriers available at all.
- dekhn 8y ago
  I developed the Exacycle idle computing system at Google and we ran extensive docking, with very good models of synthesizable drugs. Based on my in-depth knowledge of this field, along with extensive experience running a system that produced 1M CPU hours per hour 24/7 and made significant improvements in protein design and structure modelling. Using more CPU time in this would have helped a lot.
momeara 8y ago
Co-author here (AMA)--A large-scale docking screen of 116M molecules takes ~1100 cpu days on our cluster, working out to about 1 mol/sec, which is very fast for virtual screening. What this doesn't account for is this requires about 30 minutes per compound to precompute information (conformations, partial charges, etc.). So this works out to ~6M cpu/hours to prepare the library for screening, which is a substantial amount of computation. We're loading about 1M molecules a day and have a 2-3 year backlog of compounds to load from Enamine. The good news is that once the library is prepared, it is quick to screen at more targets--and we make the pre-computed library available at zinc15.docking.org. Interestingly, as the library grows a limiting factor is storing the library on disk. It is now ~20T. We've set up several mirrors around the world for groups that are actively using it. An interesting problem will be to see if preparing compounds for screening on the fly (e.g. with machine learning models) can overcome this limitation to keep up with library growth. A big question for us is what will the return on investment in screening larger and larger libraries be? One of the take aways from this work is if docking has moderate enrichment, than screening larger libraries not only gives more hits but actually can increase the hit-rate for the top scoring compounds.
- dekhn 8y ago
  I'm curious why 20 terabytes on disk is a challenge. That sounds like a bucket in S3 problem
- cing 8y ago
  I know that docking using GPU is about an order of magnitude faster than CPU (see today's Schrodinger 2019-1 release notes, https://youtu.be/K4AYdBvuOe4?t=90 https://youtu.be/K4AYdBvuOe4?t=90). Is there a way of doing GPU accelerated precomputation though?
  - momeara 8y ago
    Hey Chris--We're right now using a mix of commercial and open source software like Omega, Corina, AMSOL, and Mol2DB. Probably the slowest step is generating the partial charges for each conformer with a reasonably high quality semi-empirical forcefield. I'm not sure if there are competitive (in terms of quality) GPU based methods, but if there were methods that were ~1000 times faster as can be the case for GPU based methods, it would definitely speed up the pre-computation or make on-the-fly prep feasible. Do you have any ideas of where we should look?