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Modeling Variance: A Variance-Motivated Approach to Molecular Prediction

preprint
revised on 19.11.2020, 13:43 and posted on 20.11.2020, 04:51 by na'il mitchell
In this variance-motivated study, the variance of a multi-thousand molecular dataset of Coulomb matrices is analyzed. This paper presents novel statistical methods and models that can aide in molecular prediction and analysis. A blended statistical/ML model is introduced for classifying data as Normal as well as a model for visualizing variance. Linear regression is also used to show a potential simple and 1 dimensional molecular descriptor, for some molecules. Paper includes literature review.

History

Email Address of Submitting Author

nm@chemathon.com

Institution

Chemathon Inc

Country

USA

ORCID For Submitting Author

0000-0002-5898-5696

Declaration of Conflict of Interest

no conflict of interest

Version Notes

Paper was updated to list additional information for regression results. Typo in figure of classification tree was corrected.

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