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J Pharmacol Ther Res 2017 Volume 1 Issue 2
November 02-03, 2017 Chicago, USA
4
th
International Congress on
International Conference and Exhibition on
Drug Discovery, Designing and Development
Biochemistry, Molecular Biology: R&D
&
The dependence of the kNN-QSAR models on the initial descriptors set generation
Fatima T Adilova, Rifkat R Davronov
and
Uygun U Jamilov
Institute of Mathematics, Academy of Sciences, Uzbekistan
Statement of the Problem
: QSAR model development and
validation has led to establish a complex strategy that
can be used to prioritize the selection of chemicals for the
experimental validation. The high accuracy of the training
set model characterized with leave-one-out cross validated
R2 (q2).However, the dependence of this method on the
descriptors initial set has not been previously studied.
Methodology & Theoretical Orientation
: In this study,
following the kNN-QSAR principle, we to study the
dependence of the kNN-QSAR on the initial set of
descriptors, using of two other packages -rcdk , Dragon, and
all calculations were carried out in the system R.
Findings
: The first data set was a well-known group of ligands
of corticosteroid binding globulin. From all 320 models from
two training sets the best predictive model was characterized
by q2 = 0.74, R2 =0.86, R0 2= 0.82, RMSE = 0.04, F = 49.3,
k = 0.98 and P = 1.1 × 10−4. The second data set was the
alkaloids of harmala ordinary quinazoline structure and
derivatives. The original sample was randomly broken up
three times divided into a training, test samples, while laying
down an external sample. Three series of simulation running
were conducted, in each of which 242, 99 and 10 QSAR
models were built; the best predictive model produced from
the first training set: q2 = 0.72, R2 =0.92, R02= 0.87, RMSE =
0.005, F = 318.88, k = 1.02 and P = 6.9 × 10−7.
Conclusion & Significance
: The required dependence exists,
so it is necessary to determine the criteria for the robustness
of the models. In addition, it would be promising to study
other methods for determining the proximity and similarity
of compounds.
Speaker Biography
Adilova Fatima has completed her PhD at the age of 30 years Institute of Cybernetics,
Academy of Sciences, Uzbekistan and postdoctoral studies from the Institute of Control
Science, Russian Academy of Sciences. She is the Head of Biomedical Lab., Institute
of Mathematics , Academy of Sciences, Uzbekistan. She has published more than 60
papers in reputed journals and has been serving as an expert of State Committee of
Science &Technology.
e:
fatima
_adilova@rambler.ru