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Comparison of a Neural
Net-based QSAR Algorithm (PCANN) with Hologram- and Multiple Linear Regression-based
QSAR Approaches: Application to 1,4-dihydropyridine-based Calcium Channel Antagonists
Viswanadhan VN, Mueller GA, Basak SC, Weinstein JN.
J Chem Inf Comput Sci 2001 May-Jun;41(3):505-11
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Abstract: A QSAR algorithm (PCANN) has been developed and applied to a set of calcium channel blockers
which are of special interest because of their role in cardiac disease and also because many
of them interact with P-glycoprotein, a membrane protein associated with multidrug resistance
to anticancer agents. A database of 46 1,4-dihydropyridines with known Ca2+ channel binding
affinities was employed for the present analysis. The QSAR algorithm can be summarized as
follows: (1) a set of 90 graph theoretic and information theoretic descriptors representing
various structural and topological characteristics was calculated for each of the 1,4-dihydropyridines
and (2) principal component analysis (PCA) was used to compress these 90 into the eight best
orthogonal composite descriptors for the database. These eight sufficed to explain 96% of the
variance in the original descriptor set. (3) Two important empirical descriptors, the
Leo-Hansch lipophilic constant and the Hammet electronic parameter, were added to the list of
eight. (4) The 10 resulting descriptors were used as inputs to a back-propagation neural
network whose output was the predicted binding affinity. (5) The predictive ability of the
network was assessed by cross-validation. A comparison of the present approach with two other
QSAR approaches (multiple linear regression using the same variables and a Hologram QSAR model)
is made and shows that the PCANN approach can yield better predictions, once the right network
configuration is identified. The present approach (PCANN) may prove useful for rapid assessment
of the potential for biological activity when dealing with large chemical libraries.
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