A Performance Comparison of Two Machine Learning Models to Predict the Formation of Pharmaceutical Cocrystals

A Performance Comparison of Two Machine Learning Models to Predict the Formation of Pharmaceutical Cocrystals

Bibliographic Details
Main Authors: Urbina, Joaquin, Morgan, Paul, Moralez, Alex, Herrera, Chelsea
Format: Online
Language:eng
Published: University of Belize 2022
Online Access:https://jobr.ub.edu.bz/index.php/ubrj/article/view/1
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spelling JOBR12022-09-29T21:52:21Z A Performance Comparison of Two Machine Learning Models to Predict the Formation of Pharmaceutical Cocrystals Urbina, Joaquin Morgan, Paul Moralez, Alex Herrera, Chelsea Pharmaceutical cocrystals machine learning cocrystal prediction binary logistic regression model random forest model The use of machine learning has recently attracted the pharmaceutical industry and academia because it is able to reliably predict the cocrystal formation outcomes of API-coformer combinations and thus lead to an efficient cocrystal screening approach.  In this study, binary logistic regression and random forest models were developed with the intention of comparing their performance against predicting the cocrystal outcomes of a dataset of API-coformer combinations using a variety of inherent molecular features, and identifying which of these features tend to influence cocrystal formation more than others.  The feature importance data of both models revealed that the most basic acceptor site on an API (basic pKa1) seemed to be one of the most important features that can reliably predict the formation of cocrystals.  It was also found that the random forest model showed superior performance over the binary logistic regression model in its predictive accuracy (0.901 vs 0.811 respectively) based on the ROC plots and confusion matrices.  The cocrystal prediction power of these and other models will be further investigated by expanding the number and types of molecular properties and the size of the dataset. University of Belize 2022-09-29 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://jobr.ub.edu.bz/index.php/ubrj/article/view/1 Journal of Belizean Research; Vol. 1 No. 1 (2022): Inaugural Issue of the Journal of Belizean Research eng https://jobr.ub.edu.bz/index.php/ubrj/article/view/1/1 Copyright (c) 2022 Journal of Belizean Research https://creativecommons.org/licenses/by-sa/4.0
institution University of Belize
collection Journal of Belizean Research
language eng
format Online
author Urbina, Joaquin
Morgan, Paul
Moralez, Alex
Herrera, Chelsea
spellingShingle Urbina, Joaquin
Morgan, Paul
Moralez, Alex
Herrera, Chelsea
A Performance Comparison of Two Machine Learning Models to Predict the Formation of Pharmaceutical Cocrystals
author_facet Urbina, Joaquin
Morgan, Paul
Moralez, Alex
Herrera, Chelsea
author_sort Urbina, Joaquin
title A Performance Comparison of Two Machine Learning Models to Predict the Formation of Pharmaceutical Cocrystals
title_short A Performance Comparison of Two Machine Learning Models to Predict the Formation of Pharmaceutical Cocrystals
title_full A Performance Comparison of Two Machine Learning Models to Predict the Formation of Pharmaceutical Cocrystals
title_fullStr A Performance Comparison of Two Machine Learning Models to Predict the Formation of Pharmaceutical Cocrystals
title_full_unstemmed A Performance Comparison of Two Machine Learning Models to Predict the Formation of Pharmaceutical Cocrystals
title_sort performance comparison of two machine learning models to predict the formation of pharmaceutical cocrystals
publisher University of Belize
publishDate 2022
url https://jobr.ub.edu.bz/index.php/ubrj/article/view/1
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