Page 20 - Book of Abstracts
P. 20

th
                               8  Biannual Conference on Chemistry - CHEM 08

                        Compressibility: A Potent Descriptor for Toxicological

                                               Property Prediction

                              Hiteshi Tandon , Tanmoy Chakraborty *, Vandana Suhag
                                              1
                                                                     2
                                                                                        3
                    1 Department of Chemistry, Manipal University Jaipur, Jaipur 303007, Rajasthan,
                                                        India.
                       2 Department of Chemistry, School of Engineering, Presidency University,
                                         Bengaluru 560064, Karnataka, India.
                     3 Department of Applied Sciences, BML Munjal University, Gurugram 122413,
                      Haryana, India. Email: hiteshitandon@yahoo.co.in, tanmoychem@gmail.com;
                                       tanmoy.chakraborty@jaipur.manipal.edu.


                                                     ABSTRACT

                    Toxicity prediction is  a crucial need of the hour and  Conceptual Density
                    Functional  Theory (CDFT) based reactivity descriptors are of  extreme
                    importance in the study of toxicological behaviour. In the present work, a new
                    model of atomic compressibility (β), a CDFT-based descriptor, is proposed based
                    on the conjoint action of electrophilicity index (ω) and absolute radius (r). It is for
                    the first time while application of compressibility is being explored to determine
                    toxicological power. Computed compressibility is  employed  to  construct  a
                    quantitative  structure–activity  relationship (QSAR)  model  to study  the
                    toxicological property of 209 aliphatic organic molecules. Regression analysis is
                    performed to correlate the computed descriptor in terms of accurate and realistic
                    QSAR model. In the analysis, one parameter QSAR is developed to predict the
                    toxicological behaviour for the ciliate  T. Pyriformis. The compressibility-
                    dependent QSAR model is expressed as:
                                                        IGC50 = a β + b
                                                             -1
                    where IGC50  is the 50% inhibitory growth concentration, a measure of toxicity,
                                -1
                    and ‘a’ and ‘b’ are regression coefficients. The predicted toxicities exhibit high
                    resemblance with the observed toxicities validating our model. Further, high
                    values of coefficients of determination  (R ) and Cross-Validation coefficients
                                                              2
                    (     ) display the goodness-of-fit and robustness of our model.
                       2
                              

                    Keywords:  Compressibility,  Conceptual Density Functional  Theory (CDFT),
                    Reactivity Descriptor,  Toxicity,  Quantitative Structure  Activity Relationship
                    (QSAR)










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