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Finding Needles in a Haystack: Determining Key Molecular Descriptors Associated with the Blood‐brain Barrier Entry of Chemical Compounds Using Machine Learning
Author(s) -
Majumdar Subhabrata,
Basak Subhash C.,
Lungu Claudiu N.,
Diudea Mircea V.,
Grunwald Gregory D.
Publication year - 2019
Publication title -
molecular informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.481
H-Index - 68
eISSN - 1868-1751
pISSN - 1868-1743
DOI - 10.1002/minf.201800164
Subject(s) - haystack , molecular descriptor , cheminformatics , quantitative structure–activity relationship , random forest , set (abstract data type) , feature selection , key (lock) , software , variable (mathematics) , computer science , artificial intelligence , selection (genetic algorithm) , machine learning , data mining , chemistry , mathematics , computational chemistry , mathematical analysis , computer security , programming language
In this paper we used two sets of calculated molecular descriptors to predict blood‐brain barrier (BBB) entry of a collection of 415 chemicals. The set of 579 descriptors were calculated by Schrodinger and TopoCluj software. Polly and Triplet software were used to calculate the second set of 198 descriptors. Following this, modelling and a two‐deep, repeated external validation method was used for QSAR formulation. Results show that both sets of descriptors individually and their combination give models of reasonable prediction accuracy. We also uncover the effectiveness of a variable selection approach, by showing that for one of our descriptor sets, the top 5 % predictors in terms of random forest variable importance are able to provide a better performing model than the model with all predictors. The top influential descriptors indicate important aspects of molecular structural features that govern BBB entry of chemicals.