Current Landscape of Hierarchical QSAR Modeling and its Applications: Some Comments on the Importance of Mathematical Descriptors as well as Rigorous Statistical Methods of Model Building and Validation

Subhash C. Basak, Subhabrata Majumdar

Research output: Chapter in Book/Report/Conference proceedingChapter

5 Scopus citations

Abstract

Mathematical chemistry or more accurately discrete mathematical chemistry had a tremendous growth spurt in the second half of the twentieth century and the same trend is continuing in the twenty first century. This continual growth was fueled primarily by two major factors: 1) Novel applications of discrete mathematical concepts to chemical and biological systems, and 2) Availability of high speed computers and relevant software whereby hypothesis driven as well as discovery oriented research on large data sets could be carried out. This led to the development of not only a plethora of new concepts, but also to various useful applications. This chapter will discuss the major milestones in the development of hierarchical QSARs for the prediction of physical as well as biological properties of various classes of chemicals by the Basak group of researchers using mathematical descriptors and different statistical methods.

Original languageEnglish (US)
Title of host publicationAdvances in Mathematical Chemistry and Applications
Subtitle of host publicationRevised Edition
PublisherElsevier Inc.
Pages251-281
Number of pages31
Volume1
ISBN (Electronic)9781681081977
ISBN (Print)9781681081984
DOIs
StatePublished - Jan 1 2015

Keywords

  • Adjacency matrix
  • Big data
  • Congenericity principle
  • Connectivity indices
  • Distance matrix
  • Diversity begets diversity principle
  • E-state indices
  • Envelope models
  • Graph theoretic matrices
  • Graph theory
  • Hierarchical quantitative structure-activity relationship (HiQSAR)
  • Information theoretic indices
  • Interrelated two-way clustering
  • Leave one out (LOO) method
  • Linear discriminant analysis
  • Molecular graphs
  • Mutagenicity
  • Naïve q
  • Partial least square (PLS)
  • Principal components analysis (PCA)
  • Principal components regression (PCR)
  • Proper cross validation
  • Property-activity relationship (PAR)
  • Quantum chemical descriptors
  • Ridge regression (RR)
  • Topochemical indices
  • Topological indices
  • Topostructural indices
  • True q
  • Valence connectivity indices
  • Weighted pseudograph

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