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Driver | Alexandra McIsaac |
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Approver | Lily Wang Brent Westbrook (Unlicensed) |
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Contributors | ? | |
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Other stakeholders | David Mobley , Michael Gilson , Michael Shirts , Daniel Cole |
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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 |
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Time frame | ? |
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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
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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
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Status | | Status |
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| colour | Yellow |
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| title | In progress |
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GitHub repo | |
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Slack channel | https://openforcefieldgroup.slack.com/archives/CDR1P66Q2 |
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Designated meeting | FF fitting meeting |
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Released force field | |
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Publication | |
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In order to accurately model electrostatics, we wish to train a graph neural network charge model which solves these problems. We will train the GNN to a higher level of QM theory, to more accurately capture the electrostatics of complicated systems like hypervalent atoms. We will model the effects of solvent polarization directly by using a solvent model.
🎯 Scope
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 must 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
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Nice to have: | Expand element coverage to include B, Si, maybe metals? Incorporating virtual sites Confidence metric returned directly by neural network
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Not in scope: | |
⚙️ Project Approaches
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