Abstract
Monte Carlo method based QSAR studies for inhibitors of Mer kinase, a potential novel
target for cancer treatment, has been carried out using balance of correlation technique.
The data was divided into three random and dissimilar splits and hybrid optimal descriptors
derived from SMILES and hydrogen filled graphs based notations were used for construction
of QSAR models. The generated models have good fitting ability, robustness, generalizability
and internal predictive ability. The external predictive ability has been tested using
multiple criteria and described models exhibited good performance in all of these
tests. The values of R2, Q2, R2
test, Q2
test, R2
m and ∆R2
m for the best model are 0.9502, 0.9388, 0.9469, 0.9083, 0.7534 and 0.0894 respectively.
Also, the structural characteristics responsible for enhancement and reduction of
activity have been extracted. Further, the agreement with the OECD rules for QSAR
model has been discussed.
Key words
Mer kinase - QSAR - CORAL - SMILES - OECD