Assessment of machine learning approaches for predicting the crystallization propensity of active pharmaceutical ingredients-Reference-Cited by-同舟云学术

Assessment of machine learning approaches for predicting the crystallization propensity of active pharmaceutical ingredients

Published:2019 Issue:8 Volume:21 Page:1215-1223
ISSN:1466-8033
Container-title:CrystEngComm
language:en
Short-container-title:CrystEngComm

Author:

Ghosh Ayana¹²³⁴^ORCID,Louis Lydie¹²³⁴,Arora Kapildev K.⁵⁶⁷⁸⁴^ORCID,Hancock Bruno C.⁵⁶⁷⁸⁴^ORCID,Krzyzaniak Joseph F.⁵⁶⁷⁸⁴,Meenan Paul⁵⁶⁷⁸⁴,Nakhmanson Serge¹²³⁴^ORCID,Wood Geoffrey P. F.⁵⁶⁷⁸⁴^ORCID

Affiliation:

1. Department of Materials Science and Engineering and Institute of Materials Science

2. University of Connecticut

3. Storrs

4. USA

5. Pfizer Worldwide Research & Development

6. Pharmaceutical Sciences

7. Pfizer Inc.

8. Groton

Abstract

This work critically evaluates a number of machine learning approaches for predicting the crystallization propensity of active pharmaceutical ingredients using a real-world dataset.

Publisher

Royal Society of Chemistry (RSC)

Subject

Condensed Matter Physics,General Materials Science,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2019/CE/C8CE01589A

Reference28 articles.

1. Predicting the Crystallization Propensity of Drug-Like Molecules

2. Will it crystallise? Predicting crystallinity of molecular materials

3. A publicly available crystallisation data set and its application in machine learning

4. Machine-learning-assisted materials discovery using failed experiments

5. Significance of controlling crystallization mechanisms and kinetics in pharmaceutical systems

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