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Improving small molecule force fields by identifying and characterizing small molecules with inconsistent parameters

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Abstract

Many molecular simulation methods use force fields to help model and simulate molecules and their behavior in various environments. Force fields are sets of functions and parameters used to calculate the potential energy of a chemical system as a function of the atomic coordinates. Despite the widespread use of force fields, their inadequacies are often thought to contribute to systematic errors in molecular simulations. Furthermore, different force fields tend to give varying results on the same systems with the same simulation settings. Here, we present a pipeline for comparing the geometries of small molecule conformers. We aimed to identify molecules or chemistries that are particularly informative for future force field development because they display inconsistencies between force fields. We applied our pipeline to a subset of the eMolecules database, and highlighted molecules that appear to be parameterized inconsistently across different force fields. We then identified over-represented functional groups in these molecule sets. The molecules and moieties identified by this pipeline may be particularly helpful for future force field parameterization.

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Abbreviations

FF:

Force field

QM:

Quantum mechanical

TFD:

Torsion fingerprint deviation

RMSD:

Root-mean-square deviation

MMFF:

Merck molecular force field

GAFF:

General AMBER force field

SMIRNOFF:

SMIRKS native open force field

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Acknowledgements

JNE appreciates financial support from the National Institute of Health (R01GM108889). VTL appreciates funding from the National Science Foundation Graduate Research Fellowship Program. CCB appreciates financial support from The Molecular Sciences Software Institute under NSF Grant ACI-154758. DLM appreciates financial support from the National Institutes of Health (R01GM108889 and R01GM132386) and the National Science Foundation (CHE 1352608). The contents of this paper are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.

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Authors and Affiliations

  1. Department of Pharmaceutical Sciences, University of California, Irvine, Irvine, CA, 92697, USA

    Jordan N. Ehrman, Nam Thi, Daisy Y. Kyu & David L. Mobley

  2. Department of Chemistry, University of California, Irvine, Irvine, CA, 92697, USA

    Victoria T. Lim, Caitlin C. Bannan & David L. Mobley

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  1. Jordan N. Ehrman
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  2. Victoria T. Lim
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  4. Nam Thi
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  6. David L. Mobley
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Corresponding author

Correspondence to David L. Mobley.

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Code and data availability

We provide the code used in this project in our GitHub repository (https://github.com/mobleylab/off-ffcompare and with a DOI at https://dx.doi.org/10.5281/zenodo.3995606). Additionally, at https://dx.doi.org/10.5281/zenodo.3995059 we provide a supporting data package. This includes a .csv file which has TanimotoCombo and TFD scores, SMILES strings, and eMolecules identifiers for all 2,698,456 molecules analyzed. Additionally, we provide optimized geometries of 265,847 molecules with four or more difference flags. An archived copy of the GitHub repository is provided in the electronic Supporting Information associated with this paper.

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The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Disclosures

DLM is a member of the Scientific Advisory Board of OpenEye Scientific Software and an Open Science Fellow with Silicon Therapeutics.

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Ehrman, J.N., Lim, V.T., Bannan, C.C. et al. Improving small molecule force fields by identifying and characterizing small molecules with inconsistent parameters. J Comput Aided Mol Des 35, 271–284 (2021). https://doi.org/10.1007/s10822-020-00367-1

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