title: Development of MLR and SVM Aided QSAR Models to Identify Common SAR of GABA Uptake Herbal Inhibitors used in the Treatment of Schizophrenia.
abstract: BACKGROUND
  Alterations in GABAnergic system are implicated in the pathophysiology of schizophrenia. Available antipsychotics that target GABA receptor form a desirable therapeutic strategy in the treatment regimen of schizophrenia, unfortunately, suffer serious setback due to their prolonged side effects. The present investigation focuses on developing QSAR models from the biological activity of herbal compounds and their derivatives that promise to be alternative candidates to GABA uptake inhibitors.
  
  
  METHODS
  Three sets of compounds were undertaken in the study to develop QSAR models. The first set consisted of nine compounds which included Magnolol, Honokiol and other GABA acting established compounds. The second set consisted of 16 derivatives of N-diarylalkenylpiperidinecarboxylic acid. The third QSAR dataset was made up of thirty two compounds which were Magnolol and Honokiol derivatives. Multiple linear regressions (MLR) and support vector machine (SVM) supervised quantitative structure-activity relationship (QSAR) models were developed to predict the biological activity of these three sets. The purpose of taking three QSAR sets of diverse chemical structures but identical in their GABA targeting and pharmacological action was to identify common chemical structure features responsible for structure-activity relationship (SAR).
  
  
  RESULTS
  Linear and non-linear QSAR models confirmed that the three sets shared common structural descriptors derived from WHIM (Weighted Holistic Invariant Molecular descriptors), 3D-MoRSE and Eigenvalue classes.
  
  
  CONCLUSIONS
  It was concluded that properties like electro negativity and polarizability play a crucial role in controlling the activity of herbal compounds against GABA receptor.
authors.affiliation: Department of Biochemistry and Bioinformatics, Karpagam University, Coimbatore- 641 021, Tamil Nadu, India.|Department of Bioinformatics, SIAS-Centre for Scientific Research, Safi Institute of Advanced Study (SIAS), Rasiya Nagar, Vazhayoor East, Malappuram-673633, Kerala, India.
authors.forename: Sahila Mohammed
authors.initials: SM
authors.lastname: Marunnan
authors.affiliation: Research and Development Centre, Bharathiyar University, Coimbatore-641046, Tamil Nadu, India.
authors.forename: Babitha Pallikkara
authors.initials: BP
authors.lastname: Pulikkal
authors.affiliation: Department of Bioinformatics, SIAS-Centre for Scientific Research, Safi Institute of Advanced Study (SIAS), Rasiya Nagar, Vazhayoor East, Malappuram-673633, Kerala, India.
authors.forename: Anitha
authors.initials: A
authors.lastname: Jabamalairaj
authors.affiliation: Bioinformatics Research Laboratory, Eminent Biosciences, Vijaynagar, Indore-452010, India.
authors.forename: Srinivas
authors.initials: S
authors.lastname: Bandaru
authors.affiliation: Bioinformatics Research Laboratory, Eminent Biosciences, Vijaynagar, Indore-452010, India.
authors.forename: Mukesh
authors.initials: M
authors.lastname: Yadav
authors.affiliation: Bioinformatics Research Laboratory, Eminent Biosciences, Vijaynagar, Indore-452010, India.|In Silico Research Laboratory, LeGene Biosciences, Vijaynagar, Indore-452010, India.
authors.forename: Anuraj
authors.initials: A
authors.lastname: Nayarisseri
authors.affiliation: Department of Biochemistry, PSG College of Arts and Science, Coimbatore-641 014, India.
authors.forename: Victor Arokia
authors.initials: VA
authors.lastname: Doss
mesh_terms: D004305:Dose-Response Relationship, Drug; D058805:GABA Uptake Inhibitors / Q000737:chemistry / Q000627:therapeutic use*; D006801:Humans; D020128:Inhibitory Concentration 50; D016014:Linear Models; D008958:Models, Molecular; D028321:Plant Preparations / Q000627:therapeutic use*; D021281:Quantitative Structure-Activity Relationship; D012559:Schizophrenia / Q000188:drug therapy*; D060388:Support Vector Machine
chemical_list: D058805:GABA Uptake Inhibitors; D028321:Plant Preparations
keywords: Linear and non-linear QSAR models; MLR and SVM.; Schizophrenia