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Driver

Alexandra McIsaac

Approver

Lily Wang Brent Westbrook (Unlicensed)

Contributors

Other stakeholders

David Mobley , Michael Shirts , Daniel Cole

Objective

A neural network charge model that can assign conformer-independent charges to both small molecules and large systems, at a higher level of theory than AM1BCC

Time frame

?

Key outcomes

A neural network charge model that:

  • Is trained on data with a higher level of QM theory than AM1-BCC, with polarization effects from a solvent model

  • Can accurately assign charges to small molecules and large systems at a reasonable speed

  • Assigns charges that perform better in simulation than AM1-BCC

  • Corrects issues with sulfur and phosphorus charges

A force field incorporating:

  • NAGL2 charges

  • re-trained vdW terms

  • re-trained valence terms

Key metrics

  • Equivalent Good reproduction of the underlying data defined as equivalent or better testing error compared to NAGLon ESPs, dipoles, and quadrupoles at the NAGL2 level of theory, compared to NAGL’s testing error on AM1BCC

  • Improved performance on “real-world” benchmarks compared to NAGL/AM1BCC-ELF10 (e.g. solvation free energies, protein-ligand benchmarks, or other similar targets), especially for hypervalent atoms

Status

Status
colourYellow
titleIn progress

GitHub repo

Slack channel

https://openforcefieldgroup.slack.com/archives/CDR1P66Q2

Designated meeting

FF fitting meeting

Released force field

Publication

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Must have:

  • Neural network charge model that performs better than or equivalent to AM1BCC-ELF10 on very small molecules, small molecules, and proteins, lipids, and nucleic acids

  • Minimum element set includes all currently covered atoms

  • Charge assignment speed must be faster scale better than AM1-BCC

  • Assigned charges must reproduce QM ESPs and dipoles better than NAGL1/AM1-BCC

  • Assigned charges must reproduce “real world” benchmarks like solvation free energies and protein-ligand binding better than NAGL1/AM1-BCC

  • Must provide reasonable/physical charges for “buried atoms” e.g. atoms that are not solvent accessible and often are assigned unphysical charges with unrestrained ESP fitting methods

Nice to have:

  • Expand element coverage to include B, Si, maybe metals?

  • Incorporating virtual sites

  • Confidence metric returned directly by neural network

Not in scope:

  • Large systems that aren’t proteins, e.g. organometallics

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