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Models for Prediction of V600E BRAF and Melanoma Cells Growth Inhibitory Activities of Pyridoimidazolones
Author(s) -
Dutt R.,
Madan A. K.
Publication year - 2010
Publication title -
archiv der pharmazie
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.468
H-Index - 61
eISSN - 1521-4184
pISSN - 0365-6233
DOI - 10.1002/ardp.201000034
Subject(s) - decision tree , in silico , melanoma , molecular descriptor , mutation , computational biology , computer science , artificial intelligence , quantitative structure–activity relationship , chemistry , biology , machine learning , cancer research , biochemistry , gene
Targeted inhibition of activated BRAF mutation has emerged as a most promising and putative therapeutic approach for the anticancer drug development. In the present study, an in‐silico approach using decision tree and moving average analysis has been applied to a data set comprising of 43 analogues of pyridoimidazolones for development of models for prediction of both V600E BRAF and melanoma cells (BRAF WM266.4) growth inhibitory activities. A decision tree was mainly employed for determining the importance of molecular descriptors ( n = 46). The value of majority of these descriptors for each analogue in the dataset was computed using E‐Dragon software (version 1.0). The decision tree learned the information from the input data with an accuracy of 98% and correctly predicted the cross‐validated (10‐fold) data with accuracy up to 79%. A total of three non‐correlating descriptors, identified best by the decision tree analysis, were subsequently utilized for development of suitable models using moving average analysis. These proposed models resulted in the prediction of V600E BRAF inhibitory activity (IC 50 ) and melanoma cells growth (SRB GI 50 ) inhibitory activity with an overall accuracy of ≥90%. The statistical significance of models/descriptors was assessed through intercorrelation analysis, sensitivity, specificity and Matthew's correlation coefficient.