Modeling and predicting chiral stationary phase enantioselectivity: An efficient random forest classifier using an optimally balanced training dataset and an aggregation strategy-Reference-Cited by-同舟云学术

Modeling and predicting chiral stationary phase enantioselectivity: An efficient random forest classifier using an optimally balanced training dataset and an aggregation strategy

Published:2018-03 Issue:6 Volume:41 Page:1365-1375
ISSN:1615-9306
Container-title:Journal of Separation Science
language:en
Short-container-title:J. Sep. Sci.

Author:

Piras Patrick¹^ORCID,Sheridan Robert²,Sherer Edward C.³,Schafer Wes⁴,Welch Christopher J.⁵,Roussel Christian¹

Affiliation:

1. Aix Marseille Université; CNRS; Centrale Marseille, iSm2; Marseille France

2. Department of Structural Chemistry; Merck Research Laboratories; Rahway USA

3. Modeling and Informatics Process Research and Development; Merck Research Laboratories; Rahway USA

4. Department of Process & Analytical Chemistry; Merck Research Laboratories; Rahway NJ USA

5. Welch Innovation, LLC; Cranbury USA

Publisher

Wiley

Subject

Filtration and Separation,Analytical Chemistry

Reference23 articles.

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2. Current challenges and future prospects in chromatographic method development for pharmaceutical research;Mattrey;Trends in Anal. Chem.,2017

3. Random forests;Breiman;Machine Learning,2001

4. Random forest: a classification and regression tool for compound classification and QSAR modeling;Svetnik;J. Chem. Inf. Comput. Sci.,2003

5. Random forest models to predict aqueous solubility;Palmer;J. Chem. Inf. Model.,2007

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