Page Comparison

10

WBO interpolated parameters update

Jessica &
Pavan

• DM – Datasets to remove – Will want to remove fragmenter and theory benchmarks. We DO want to include SiliconTX set.

• CB – On slide 4, the pink points make it clear that some parameter splitting will be needed. Specifically t69. Currently it mixed ureas (where N is bound to O) and amidea (where N isn’t bound to O).

  • HJ – I wonder if this can be resolved with new torsion parameters I came up with for the amide issue. We came up with a child parameter of t69.

  • DM – I still think we should use CB’s original suggestion, but we could compare those to HJ’s/openff-1.3.0’s SMARTS

  • CB – In Rowley set in particular, we’re likely to see things like bonds coming off imidazole N’s. The current form of t69 will group this with amides, and that’s clearly a bad idea.

• LPW – On R^2 table, is there anything that the molecules in the bottom right share?

  • JM – I’d like to look into this

• TG – The torsiondrives in the 80-100 range maybe due to faulty data (like, driving torsions inside of a ring)

  • DM – We looked at these in particular and they are valid.

10

amide update and ic hessian fitting

Hyesu Jang

IC hessian implementation in ForceBalance

• First few rows/columns are stiffer degrees of freedom(bonds and angles) and show the largest magnitudes.

Target weights for different target types

• CB – With candidate weights, why not use 1/median?

  • HJ – That’s a good idea. My weight candidates are just intended to be rough guesses. I agree that being more precise would be good

• TG – Since we have so many optimizations, I wonder if their contributions will always dominate

  • HJ – Agreed. I’d kinda like to have a way to apply similar weights to each TARGET, rather than target category.

  • LPW – I think that changing the distribution of targets COULD change the output of the fit. Though I hope that we can generate roughly equal amounts of data for each target type. However, since TDs are so expensive, we can’t expect that to be the case.

  • DM – Now that lots of people are trained to fit FFs, we should be able to explore these questions.

  • CB – I see a tension between the academic interest to get the method correct, and the industry perspective of “getting better binding free energies”. In the hessian, we see a dominance of contributions from bonds and angles. However, these won’t make a big difference in binding FEs. So I’m inclined to think that we want TDs to be VERY heavily weighted.

Amide update

• DM – Are we ready to release?

  • HJ – We’ve changed some prior widths, which may have far-reaching effects, so I’d like to do a bit more testing.

  • Hyesu Jang will send a preliminary amide fix file to Arjun Naranyan

20

Standard benchmarks

Jeff and Josh

• Which metrics?

  • benchmarkff dataset

  • Hyesu’s pre-release benchmarks

• Which data and software?

  • Prepare a clone of QCA data for local running/comparison (benchmarkff)

  • Make sure everyone can run Hyesu’s pre-release benchmarks

• Short-term solution vs. long-term solution?

  • Short term: Exactly the dataset/software used in existing studies

  • Long term: Carefully curated benchmark set, potentially hidden from team. Rules about which overlapping data can be used during training.

    • Could have tool to compare degree of overlap