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

preprint
submitted on 06.02.2020 and posted on 07.02.2020 by Christian Devereux, Justin Smith, Kate Davis, Kipton Barros, Roman Zubatyuk, Olexandr Isayev, Adrian Roitberg

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, 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 non-bonded interactions. ANI-2x is shown to accurately predict molecular energies compared to DFT with a ~106 factor speedup and a negligible slowdown compared to ANI-1x. The resulting model is a valuable tool for drug development that can potentially replace both quantum calculations and classical force fields for myriad applications.

Funding

D3SC: CDS&E: Collaborative Research: Development and application of accurate, transferable and extensible deep neural network potentials for molecules and reactions

Directorate for Mathematical & Physical Sciences

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D3SC: CDS&E: Collaborative Research: Development and application of accurate, transferable and extensible deep neural network potentials for molecules and reactions

Directorate for Mathematical & Physical Sciences

Find out more...

History

Email Address of Submitting Author

roitberg@ufl.edu

Institution

University of Florida

Country

USA

ORCID For Submitting Author

0000-0003-3963-8784

Declaration of Conflict of Interest

None

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in Journal of Chemical Theory and Computation

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