2023-11-07 alchemiscale : user group meeting notes

• user questions/issues/feature requests

  • MO : is there a way to get values from individual tasks

  • estimates = []
    tf = asc.get_transformation(tf_sk)
    for pdr in asc.get_transformation_results(tf_sk, return_protocoldagresults=True):
        pr = tf.gather([pdr])
        estimates.append(pr.get_estimate())

    estimates = []
    tf = asc.get_transformation(tf_sk)
    
    for task_sk in asc.get_transformation_tasks(tf_sk):
        pdrs = asc.get_task_results(task_sk)
        pr = tf.gather(pdrs)
        estimates.append(pr.get_estimate())

    • IA : in 0.14.0 there’s a way to get the individual estimates

    • JS : very keen on openfe 0.14.0

      • IA : might want to do 0.14.1

      • JC : what are we using for serialization?

        • MH : use pydantic data models, but also use custom hooks for objects that don’t have a native representation

        • JC : is there a binary representation for these?

        • MH : currently in the process of reworking some of the storage bits,

• compute expansion

  • JC : may be useful to have an OMSF-wide allocation; since it now holds a large NSF grant, it can probably easily get one of these

    • JC : also haven’t reached out for a while to AWS or Google Cloud, but could probably ask for $40k at a time

      • would need to be ready to go; usually a time limit of about 6-12 months

      • may make sense to try getting things working with current resources

        • could make use of Moonshot Science paper publicity to rally support

      • DD : need one of the following to make good use of Spot for GPU compute:

        • extends support in protocols, allowing for chaining of short runs

        • partial execution of ProtocolDAGs in alchemiscale; possible but harder and will take more time to achieve

    • DD : having extends support in NonEquilibriumCyclingProtocol is something I’m planning to put effort on soon; needed by ASAP and for Folding@Home use

      • JC : is an extends-able RelativeHybridTopologyProtocol something Ivan and I can work on?

        • IA : moving RelativeHybridTopoologyProtocol out of openfe in the next couple weeks into feflow, so will be ready for refactor work after this

    • 
      

protein-ligand-benchmark:

• JC : [presentation slides] on changing the strategy for protein-ligand-benchmark

  • https://docs.google.com/presentation/d/1yvHggJdbrTVlgYuZXlLusm3FhLuXHPLfedgo50OWM0k/edit#slide=id.p

  • JS : are you sure about wanting to use SpruceTK?

    • JC : yes, have a lot of control of the behavior; for the short term is the way to go

      • long term interested in replacing things with some diffusion model, but have to start somewhere

      • would like to move on from Schrodinger tools here

    • JS : are you planning to expand protonation states for all of these targets?

      • JC : if ligands have favorable protonation states, then have multiple states

        • won’t include these in core dataset

  • IA : are you targeting protein-ligand-benchmark 0.3.0?

    • JC : would be next major iteration, whatever we call that

  • IA : various groups want to get involved in PLB, including OpenEye

    • OE interested in membrane proteins; would this work for those?

    • JC : a lot of the systems from BindingDB are membrane protein systems

      • don’t have to bias away from membranes

    • JC : ideally would like to focus on where in the process we observe issues in inputs

      • can then flag these upstream so they can be fixed

• IA : one of the issues we have is many folks have created their own forks of protein-ligand-benchmark

  • so fragmentation issue currently

  • JC : there is no way to really go backwards, and that’s okay

    • would be possible to pull previous versions, but no fixes backported; we move forward only

    • would also be positioned to run with Folding@Home in an automated fashion, could hook up an automated dashboard for this

  • DD : benchmarking which protocols, which FFs, etc?

    • JC : would use current best practices to first identify well-behaved targets

      • those well-behaved targets would function as a snapshot/version of the benchmark set

      • actors like OpenFF could then use individual benchmark set versions as the basis for their own benchmarks to resolve questions on FF improvement

      • others could use these set versions for their own method improvements