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High‐dimensional QSAR prediction of anticancer potency of imidazo[4,5‐b]pyridine derivatives using adjusted adaptive LASSO
Author(s) -
Algamal Zakariya Yahya,
Lee Muhammad Hisyam,
AlFakih Abdo M.,
Aziz Madzlan
Publication year - 2015
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.2741
Subject(s) - quantitative structure–activity relationship , lasso (programming language) , consistency (knowledge bases) , selection (genetic algorithm) , pyridine , feature selection , regression , computer science , mathematics , chemistry , artificial intelligence , machine learning , statistics , medicinal chemistry , world wide web
In high‐dimensional quantitative structure–activity relationship (QSAR) studies, identifying relevant molecular descriptors is a major goal. In this study, a proposed penalized method is used as a tool for molecular descriptors selection. The method, called adjusted adaptive least absolute shrinkage and selection operator (LASSO) (AALASSO), is employed to study the high‐dimensional QSAR prediction of the anticancer potency of a series of imidazo[4,5‐b]pyridine derivatives. This proposed penalized method can perform consistency selection and deal with grouping effects simultaneously. Compared with other commonly used penalized methods, such as LASSO and adaptive LASSO with different initial weights, the results show that AALASSO obtains the best predictive ability not only by consistency selection but also by encouraging grouping effects in selecting more correlated molecular descriptors. Hence, we conclude that AALASSO is a reliable penalized method in the field of high‐dimensional QSAR studies. Copyright © 2015 John Wiley & Sons, Ltd.