A random forest model for predicting crystal packing of olanzapine solvates-Reference-Cited by-同舟云学术

A random forest model for predicting crystal packing of olanzapine solvates

Published:2018 Issue:28 Volume:20 Page:3947-3950
ISSN:1466-8033
Container-title:CrystEngComm
language:en
Short-container-title:CrystEngComm

Author:

Bhardwaj Rajni M.¹²³⁴⁵^ORCID,Reutzel-Edens Susan M.⁵⁶⁷^ORCID,Johnston Blair F.¹²³⁴⁸,Florence Alastair J.¹²³⁴⁸

Affiliation:

1. Strathclyde Institute of Pharmacy and Biomedical Sciences

2. University of Strathclyde

3. Glasgow G4 0RE

4. UK

5. Eli Lilly and Company

6. Indianapolis

7. USA

8. EPSRC Centre for Continuous Manufacturing and Crystallisation (CMAC)

Abstract

A random forest (RF) classification model obtained from physicochemical properties of solvents and crystal structures of olanzapine has for the first time enabled the prediction of 3-D crystal packings of solvates. A novel solvate was obtained by targeted crystallization from the solvent identified by RF model.

Funder

Commonwealth Scholarship Commission

Engineering and Physical Sciences Research Council

Publisher

Royal Society of Chemistry (RSC)

Subject

Condensed Matter Physics,General Materials Science,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2018/CE/C8CE00261D

Reference22 articles.

1. Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling

2. Random Forest Models To Predict Aqueous Solubility

3. Random Forest Prediction of Mutagenicity from Empirical Physicochemical Descriptors

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