8.
QSPR and ANN Studies on Prediction of Aqueous
Solubility of Heterogeneous Set of Organic Compounds
Bruno Louisa, Jyoti Singhb,
Basheerulla Shaikb,
Vijay K. Agrawalb*,
Padmakar V. Khadikarc
aDepartment of Pharmacy,PO Box 38, Sultan Qaboos
University Hospital, Al Khod Muscat 123,Oman,
E-mail:
louisb4425@yahoo.com
b*QSAR and Computer Chemical Laboratories, A.P.S.
University, Rewa-486 003, India
E-mail:
apsvkal@yahoo.co.in
c Research Division, Laxmi Fumigation and Pest Control
Pvt. Ltd.
3 Khatipura, Indore-452 007, India
E-mail:
pvkhadikar@rediffmail.com
Abstract: The aqueous solubility
of 399 heterogeneous organic compounds was predicted by quantitative
structure–property relationship (QSPR) method. In this work, only
topological descriptors and indicator parameters were used. The
topological descriptors used are whole molecular structural
descriptors derived from theoretical molecular calculations (not
atom or bond count).
The multiple linear regression (MLR) (for 398
compounds R = 0.898) and artificial neural network (ANN) techniques
were used to build linear and nonlinear models, respectively. In
this work the proposed QSPR models, both by MLR and ANN, contain
identical descriptors. Comparison of these two methods reveals that
those obtained by the ANN model are better.
Keywords: Artificial neural
network, solubility, topological descriptors, Regression analysis,
QSPR
<<< |