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

In the current report, three machine learning approaches were assessed for their ability to predict the crystallization propensities of a set of small organic compounds (

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Bibliographic Details
Published in:CrystEngComm 2019, Vol.21 (8), p.1215-1223
Main Authors: Ghosh, Ayana, Louis, Lydie, Arora, Kapildev K, Hancock, Bruno C, Krzyzaniak, Joseph F, Meenan, Paul, Nakhmanson, Serge, Wood, Geoffrey P. F
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Computation
Crystallization
Datasets
Format
Impurities
Machine learning
Mathematical models
Model accuracy
Neural networks
Organic compounds
Pharmaceuticals
Properties (attributes)
Solvents
Supersaturation
Support vector machines
Training
Two dimensional models
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Description
Summary:In the current report, three machine learning approaches were assessed for their ability to predict the crystallization propensities of a set of small organic compounds (
ISSN:1466-8033
1466-8033
DOI:10.1039/c8ce01589a