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Quantitative Structure-Activity/Property/Toxicity Relationships
Figure 10. Plots between experimental and calculated toxicity of CPs against (A) D. magna, (B) B. rerio,
and (C) Bacillus
(Reprinted with permission from Padmanabhan et al., 2006c. Copyright © 2006, American Chemical Society).
92.8% variation data (r
2
= 0.878) having correlation coefficient value (r) of 0.964 (see Figure 10(B))
cv
whereas in case against Bacillus this model was able to describe maximum variation in data (91.9%)
2
along with r
value of 0.863 and r value of 0.959 (see Figure 10(C).
cv
Therefore, this result implied that group philicity along with electrophilicity are very much effective in representing the toxicity of CPs against D. magna, B. rerio, and Bacillus. It in turn showed the
importance of these reactivity indices as effective descriptors in ecotoxicological studies.
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Quantitative Structure-Activity/Property/Toxicity Relationships
6. EFFECT OF CHOSEN FUNCTIONALS IN BUILDING GLOBAL
REACTIVITY DESCRIPTORS BASED QSAR/QSPR/QSTR MODELS
Vijayaraj et al. (2009) assessed the effect of studied functionals on the ionization potential (IP) and the
electron affinity (EA) of the systems, which were generally used to compute the CDFT based reactivity
descriptors. Therefore, the dependence of IP and EA on the chosen functionals would raise questions
about the level of theory chosen to construct QSAR, QSPR and QSTR based on the reactivity descriptors. In other words, the predictive power of a regression model might depend on the chosen level of
theory. In order to explore the efficacy of various density functionals along with different basis sets in
calculating IP and EA, hence, to benchmark, systematic computations were performed considering a
series of chlorinated benzenes (CBs). Some common functionals of pure, GGA, hybrid, and meta and
hybrid meta GGA types were employed. It was found that a good agreement between computed and
experimental IP and EA values could be attained with the increase in the rigor of the level of computation. The prediction of IP and EA of CBs, computed using both Koopmans’ approximation and ΔSCF
method, was most accurately made by the use of M05-2X functional. Further, in between Koopmans’
approximation and ΔSCF method, IP and EA computed by ΔSCF method matched more closely with
the corresponding experimental values than that by the Koopmans’ approximation.
Now, using those descriptors computed at different levels of theory, different regression models were
designed to predict the toxicity of nine CBs against Poecilia reticulata. Different descriptors along with
different approximations were included. It was found that although the computation of global reactivity descriptors at different levels and/or different basis sets yielded considerably different values, the
mean deviation of a certain property among all the studied systems with respect to the chosen method/
basis set was comparable. It meant that with the change in studied method and/or basis set, the values of
computed global reactivity descriptors would be changed, however, the magnitude of the difference in
between the systems were more or less same in all the methods. This finding was important in this sense
that the efficacy of a regression model and hence the correlation coefficient would not alter significantly
with the change in level of theory. For example, in spite of good agreement with the experimental IP
and EA using ΔSCF method, it did not improve the correlation coefficient with respect to that obtained
using Koopmans’ theorem. In general, to construct a QSAR/QSPR/QSTR model, one has to consider
a data set containing a large number of systems. Therefore, it can be concluded that if one considers
computationally a very cheap level of theory to model QSAR/QSPR/QSTR, it can be possible to get
reliable results similar to the one constructed from the results of a high level of theory, which will in
deed save a lot of computational time.
7. CONCLUSION
In order to better understand the variation of property, activity and toxicity with the structural change
of a molecular motif, the Quantitative-Structure-(Property/Activity/Toxicity) Relationship models are
widely applied. Various types of methods and models were employed to construct effective regression
equations. Both, global and local reactivity descriptors based on Conceptual Density Functional Theory
are found to be very effective indices in order to predict biological activity or property or toxicity
of a set of systems. We have specially drawn attention on the efficacy of the regression models built
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157
Quantitative Structure-Activity/Property/Toxicity Relationships
based on the different combinations of the parameters viz., electrophilicity index, net electrophilicity index, group philicity along with their local variants, atomic charge, number of carbon or Cl or
non-hydrogenic atoms, atomic number, chemical potential, LUMO energy and Hartree-Fock energy
in predicting the toxicity of various aliphatic compounds, polyaromatic hydrocarbons, chloroanilines,
phenol, nitrobenzene, benzonitrile-derivatives, halogen, sulfur and chlorinated aromatic compounds,
testosterone, estrogen derivatives, and different alkali, alkaline-earth, transition-metal, arsenic ions
as well. Although individual descriptors were found to vary with the change in level of theory and /or
basis set, the efficacy of the overall QSAR/QSPR/QSTR models depend hardly on the level of theory.
Therefore, very low level of theory can also yield reliable results.
Therefore, the reactivity descriptors within the purview of conceptual DFT will be a good choice in
predicting the desired properties of a set of systems.
ACKNOWLEDGMENT
We are grateful to Professor Kunal Roy for inviting us to contribute this chapter. PKC would like to thank
DST, New Delhi for the J. C. Bose National Fellowship. SP thanks CSIR, New Delhi for his fellowship.
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