| Summary: | In the present paper, the antifungal activity of a series of benzoxazole and
oxazolo[ 4,5-b]pyridine derivatives was evaluated against Candida albicans by
using quantitative structure-activity relationships chemometric methodology
with artificial neural network (ANN) regression approach. In vitro antifungal
activity of the tested compounds was presented by minimum inhibitory
concentration expressed as log(1/cMIC). In silico pharmacokinetic parameters
related to absorption, distribution, metabolism and excretion (ADME) were
calculated for all studied compounds by using PreADMET software. A
feedforward back-propagation ANN with gradient descent learning algorithm was
applied for modelling of the relationship between ADME descriptors
(blood-brain barrier penetration, plasma protein binding, Madin-Darby cell
permeability and Caco-2 cell permeability) and experimental log(1/cMIC)
values. A 4-6-1 ANN was developed with the optimum momentum and learning
rates of 0.3 and 0.05, respectively. An excellent correlation between
experimental antifungal activity and values predicted by the ANN was obtained
with a correlation coefficient of 0.9536. [Projekat Ministarstva nauke
Republike Srbije, br. 172012 i br. 172014]
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