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Extending the Applicability of the ANI Deep Learning Molecular Potential to Sulfur and Halogens

  • Christian Devereux
    Christian Devereux
    Department of Chemistry, University of Florida, Gainesville, Florida 32611, United States
  • Justin S. Smith*
    Justin S. Smith
    Center for Non-Linear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
    Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
    *Email: [email protected]
  • Kate K. Huddleston
    Kate K. Huddleston
    Department of Chemistry, University of Florida, Gainesville, Florida 32611, United States
  • Kipton Barros
    Kipton Barros
    Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
  • Roman Zubatyuk
    Roman Zubatyuk
    Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
  • Olexandr Isayev*
    Olexandr Isayev
    Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
    *Email: [email protected]
  • , and 
  • Adrian E. Roitberg*
    Adrian E. Roitberg
    Department of Chemistry, University of Florida, Gainesville, Florida 32611, United States
    *Email: [email protected]
Cite this: J. Chem. Theory Comput. 2020, 16, 7, 4192–4202
Publication Date (Web):June 16, 2020
https://doi.org/10.1021/acs.jctc.0c00121
Copyright © 2020 American Chemical Society

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    Abstract

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    Machine learning (ML) methods have become powerful, predictive tools in a wide range of applications, such as facial recognition and autonomous vehicles. In the sciences, computational chemists and physicists have been using ML for the prediction of physical phenomena, such as atomistic potential energy surfaces and reaction pathways. Transferable ML potentials, such as ANI-1x, have been developed with the goal of accurately simulating organic molecules containing the chemical elements H, C, N, and O. Here, we provide an extension of the ANI-1x model. The new model, dubbed ANI-2x, is trained to three additional chemical elements: S, F, and Cl. Additionally, ANI-2x underwent torsional refinement training to better predict molecular torsion profiles. These new features open a wide range of new applications within organic chemistry and drug development. These seven elements (H, C, N, O, F, Cl, and S) make up ∼90% of drug-like molecules. To show that these additions do not sacrifice accuracy, we have tested this model across a range of organic molecules and applications, including the COMP6 benchmark, dihedral rotations, conformer scoring, and nonbonded interactions. ANI-2x is shown to accurately predict molecular energies compared to density functional theory with a ∼106 factor speedup and a negligible slowdown compared to ANI-1x and shows subchemical accuracy across most of the COMP6 benchmark. The resulting model is a valuable tool for drug development which can potentially replace both quantum calculations and classical force fields for a myriad of applications.

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    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jctc.0c00121.

    • Breakdown of the conformer scoring results for each molecule; MAE and RMSE for nonbonded interaction energies including deformation energies for the X40 data set and a breakdown by the interaction type for the Halgren data set; box-and-whisker plot comparing DFT, ANI-2x, and OPLS on the Genetech torsion benchmark; comparison of ANI-2x’s error and ensemble standard deviation; hyperparameters and network architecture used to train ANI-2x; and description of the COMP6v2 benchmark (PDF)

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