Finding Needles in a Haystack: Determining Key Molecular Descriptors Associated with the Blood-brain Barrier Entry of Chemical Compounds Using Machine Learning

Subhabrata Majumdar, Subhash C. Basak, Claudiu N. Lungu, Mircea V. Diudea, Gregory D. Grunwald

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Article number1800164
JournalMolecular Informatics
Volume38
Issue number8-9
DOIs
StatePublished - Aug 1 2019

Bibliographical note

Publisher Copyright:
© 2019 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim

Keywords

  • blood-brain barrier
  • machine learning
  • molecular descriptors
  • quantitative structure-activity relationship (QSAR)
  • random forest
  • two-deep cross validation
  • variable selection

Fingerprint

Dive into the research topics of 'Finding Needles in a Haystack: Determining Key Molecular Descriptors Associated with the Blood-brain Barrier Entry of Chemical Compounds Using Machine Learning'. Together they form a unique fingerprint.

Cite this