NDLI: Models for the prediction of PPARs agonistic activity of indanylacetic acids

Content Provider	Springer Nature Link
Author	Dutt, Rohit Madan, A. K.
Copyright Year	2012
Abstract	With the growth of combinatorial chemistry methods in drug discovery, a huge number of compounds are synthesized and screened in parallel for in vitro pharmacological activity, which surprisingly increased the demands of efficient mathematical models to predict desired biological activity. In the present study, an in silico approach using moving average analysis has been applied to a dataset comprising 73 analogues of indanylacetic acid for development of mathematical models for the prediction of each PPAR subtype as well as pan agonistic activity. The value of majority of molecular descriptors (n = 50) for each analogue in the dataset was computed by means of E-Dragon software (version 1.0). Three molecular descriptors, i.e. eccentric adjacency topochemical index-3, information content index-neighbourhood symmetry of 5-order and 2-path kier alpha-modified shape index, yielded the best PPAR subtype specific and sum/pan models by means of moving average analysis. The overall accuracy of the prediction for all individual mathematical models proposed with regard to PPAR α, γ and δ agonistic activity was found to be ≥87, ≥93 and ≥83 %, respectively. Surprisingly, high predictability of the order of ≥86 % was found in the case of sum/pan models. The statistical significance of models/indices was assessed through intercorrelation analysis, sensitivity, specificity and Matthew’s correlation coefficient. High predictability authenticates proposed models for prediction of each PPAR subtype (α/γ/δ) specific as well as pan agonistic activity.
Starting Page	3213
Ending Page	3228
Page Count	16
File Format	PDF
ISSN	10542523
Journal	Medicinal Chemistry Research
Volume Number	22
Issue Number	7
e-ISSN	15548120
Language	English
Publisher	Springer-Verlag
Publisher Date	2012-11-17
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Classification models E-Dragon software PPAR pan agonist Moving average analysis Topological descriptors Pharmacology/Toxicology Biochemistry Cell Biology
Content Type	Text
Resource Type	Article
Subject	Organic Chemistry Pharmacology, Toxicology and Pharmaceutics

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