Prediction of mutagenicity of chemicals from their calculated molecular descriptors: A case study with structurally homogeneous versus diverse datasets

Subhash C. Basak, Subhabrata Majumdar

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

Variation in high-dimensional data is often caused by a few latent factors, and hence dimension reduction or variable selection techniques are often useful in gathering useful information from the data. In this paper we consider two such recent methods: Interrelated two-way clustering and envelope models. We couple these methods with traditional statistical procedures like ridge regression and linear discriminant analysis, and apply them on two data sets which have more predictors than samples (i.e. n << p scenario) and several types of molecular descriptors. One of these datasets consists of a congeneric group of Amines while the other has a much diverse collection compounds. The difference of prediction results between these two datasets for both the methods supports the hypothesis that for a congeneric set of compounds, descriptors of a certain type are enough to provide good QSAR models, but as the data set grows diverse including a variety of descriptors can improve model quality considerably.

Original languageEnglish (US)
Pages (from-to)117-123
Number of pages7
JournalCurrent computer-aided drug design
Volume11
Issue number2
DOIs
StatePublished - Sep 1 2015

Bibliographical note

Publisher Copyright:
© 2015 Bentham Science Publishers.

Keywords

  • Congenericity principle
  • Diversity begets diversity principle
  • Envelope models
  • Hierarchical quantitative structure-activity relationship (HiQSAR)
  • Interrelated two-way clustering
  • Linear discriminant analysis
  • Mutagenicity
  • Ridge regression
  • Topological indices

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