Individual updates | SB Mainly working on debugging issues with FE calcs using a different functional form. The custom nonbonded force we’re working on has a default softcore functionality. So you shouldn’t need to modify it to do FE calcs. But it turns out that the soft core potential insde the potential is more like a hard core in terms of the problems that come up. So I’m working with JHorton to figure out workarounds. JW – What’s the distinction between the X-core func forms? SB – I use it to mean “when you’re turning off interactions, and molecules need to semi-overlap, the LJ terms need to go to a very small number smoothly”. If you don’t satisfy this well, you end up getting poor sampling of certain states. It’s possible that modifying some global parameters could help here too, so we’re still looking into it. JS – Double expoential form: Is that similar to buckingham potential? SB – I think so - In buckingham you replace the r^12 term with an exponential. So here we’re experimenting with replacing both r^12 and r^6. By replacing r^6 we get nicer behavior at short range.
PB – When you have time, could you check the PR on plotmol? | Github link macro |
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| link | https://github.com/SimonBoothroyd/plotmol/pull/8 |
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Building training sets for refitting charge models - I’m putting together a set of molecules that we can compute electrostatic potentials for - Talked about how to put this together last week, decided that we’ll generate conformers and optimize offline, then submit to QCF to generate and store the wavefunctions online. I uploaded a tool called splore to conda for investigating molecule datasets
CC Made a new version of the dipeptide torsiondrive set. Goal was to expand number of sidechains. There’s one torsiondrive running for all 20 amino acids, and 6 protonation states. Saw one comment by PB about renaming one file from that PR, otehr than that it should eb good to go Will start playing around with fits in forcebalance. Will plan to mimic the terms in amber FF14SB initially. SB – More details? As in functional forms, or parameter smirks? CC – The fitting model - We want to fit terms for phi and psi, but we don’t know whether those should be shared between similar amino acids. SB – That makes sense. So this would start with the null model and see whether groupings provide an advantage? CC – Yes.
Starting to work on deeper review of paper, provide first round of feedback. Talked a few weeks ago about deriving charges for ELF10 - OE COOH issue. Is this still in progress? JW – I assigned this to JMitchell but he’s been offline for a few weeks. CC – I’d use this for deriving AM1BCC charges for making the protein librarycharges. This will become blocking in a ~month. SB – OE may have fixed this internally ina recent release? Alternatively, CBayly should have code for a workaround for this somewhere in the issue tracker.
MT General update - NEP29 recommended moving projects to Python 3.8+ on Dec 26 2021. I’ve updated my packages, but if anyone needs 3.7 support I may be able to support it but you’ll need to tell me. Was getting back into the swing of things last week. Tried to fix an entry points/plugin thing that JChodera pointed out, once that’s resolved we can release OFFTK 0.10.2. New interchange release will come with OFFTK 0.10.2 Been working on improving speed for export to large files. Won’t aim to be as fast as ParmEd initially, but working on reducing the time to write a gro file down from 10 minutes to something reasonable. Started identifying which packages will need to be updated for 0.11.0 release w/ topology refactor. We’ll be opening a bunch of PRs once the release candidate toolkit package is available – Largely due to topology refactor, but openff-units will also require changes. I took responsiiblity for evaluator and forcebalance, JW has responsibility for others.
DD JS Working on forcebalance fitting to GB parameters - Getting good results. Will be doing more testing, will present at FF release call in the future. Suggestions for protein ligand benchmark sets? | Github link macro |
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| link | https://github.com/openforcefield/protein-ligand-benchmark |
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JS – Thinking of making an openmm plugin, does anyone here have experience?
PB Theory benchmark work, some analysis on the subsets and it looks like our current default is not as good with charged molecules, will be testing out a potential replacement this week. Althoug out of scope, I tried to test the DeepMind21 functional, didn't work right out of the box, need to get familiar with pyscf first (initial guess, convergence criteria, etc.) to be on par with our current psi4 calculations. Did some fits with impropers for Jessica's work. Spent some time on compiling and testing xtb's fix for GFN-FF but it didn't work for me. I posted my test but the developer closed it without any response, assuming it works now. Some QCA work following up on discussions with JC.
JW – I’ll be out for jury duty tomorrow Continuing to work on topology refactor - getting rdkit specific code replicated by OE and into appropriate classes ensuring that residue data goes to/from openmm/rdkit/OE in a sane, documented way Turning from_pdb into a "one stop shop" that can handle a mix of small molecules and biopolymers Making errors more informative to help users with debugging
Updating roadmap items to each have at least a primary driver and primary stakeholder/approver, and for items that are already in progress, some form of a spec for what the minimum product is and what stage are anticipated in its development The preferred python plugin/entry point system recently had a small change, but adjusting to the new system required the redesign of our tests for parameterhandler plugins (OFFTK #1163). This is the last blocker to the 0.10.2 release Discussing requirements for 0.11.0 and how to incorporate Interchange. Big discussion on thread in #developers. DD – Does the toolkit use interchange or does interchange use the toolkit? JW – It’s mostly that the toolkit uses interchange MT – Agree, this only gets a little complex when interchange uses toolkit’s parameterhandler’s find_matches method. DD – Ok, so it kinda goes both ways. MT – Yeah, it’d be super hard to fully make the dependency unidirectional.
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