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1.  Computer simulation of a novel pharmaceutical silicon nanocarrier 
We show the potential of the nanosilicon structure of the frustules of a typical diatom, Cymatopleura sp., as a new vehicle for drugs. The basic diatom nanostructure is a lattice of SiO2, and computerized methods in a dock project have identified the most likely and the best drug types to load into such a structure.
doi:10.2147/NSA.S8378
PMCID: PMC3781697  PMID: 24198479
diatom; docking; artificial neural network; simulation; computerized ethods
2.  Modeling of thermodynamic and physico-chemical properties of coumarins bioactivity against Candida albicans using a Levenberg–Marquardt neural network 
In recent years, due to vital need for novel fungicidal agents, investigation on natural antifungal resources has been increased. The special features exhibited by neural network classifiers make them suitable for handling complex problems like analyzing different properties of candidate compounds in computer-aided drug design. In this study, by using a Levenberg–Marquardt (LM) neural network (the fastest of the training algorithms), the relation between some important thermodynamic and physico-chemical properties of coumarin compounds and their biological activities (tested against Candida albicans) has been evaluated. A set of already reported antifungal bioactive coumarin and some well-known physical descriptors have been selected and using LM training algorithm the best architecture of neural model has been designed for forecasting the new bioactive compounds.
PMCID: PMC3170013  PMID: 21918627
Levenberg/Marquardt algorithm; coumarin; neural network

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