MULTIVARIANT QSAR MODEL FOR SOME POTENT COMPOUNDS AS POTENTIAL ANTI-TUMOR INHIBITORS: A COMPUTATIONAL APPROACH

Yıl 2019, Cilt: 3 Sayı: 1, 38 - 46, 15.06.2019

Shola Elıjah Sani Uba Adamu Uzaıru

https://doi.org/10.33435/tcandtc.458664

Cited By: 1

Öz

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 (R_adj) 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.

Anahtar Kelimeler

Keywords: Ciprofloxacin, Descriptors, Genetic Function Algorithm, tumor

Kaynakça

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