ABSTRACT
A
computational approach was employed to develop multivariate QSAR model to
correlate the chemical
structures
of the
ciprofloxacin
analogues with
their
observed activities
using
a
theoretical approach.
Genetic
Function Algorithm (GFA) and Multiple Linear Regression Analysis (MLRA) were
used to select the descriptors and to generate the correlation QSAR models that
relate the activity values against tumor with the molecular
structures of the active molecules. The models
were validated and the best model selected has squared correlation coefficient
(R2)
of 0.990531,
adjusted squared correlation
coefficient (Radj) of 0.95962 and Leave one out (LOO) cross validation coefficient () value of 0.942963.
The external validation set used for confirming the predictive power of the
model has its R2pred of 0.8486. Stability and
robustness of the model obtained by the validation test indicate that the model
can be used to design and synthesis other ciprofloxacin derivatives with
improved anti-tumor activity.
Primary Language | English |
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Journal Section | Research Article |
Authors | |
Publication Date | June 15, 2019 |
Submission Date | September 10, 2018 |
Published in Issue | Year 2019 Volume: 3 Issue: 1 |
Journal Full Title: Turkish Computational and Theoretical Chemistry
Journal Abbreviated Title: Turkish Comp Theo Chem (TC&TC)