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• W2030892568 abstract "A quantitative structure-permeability relationship was developed using Artificial Neural Network (ANN) modeling to study penetration across a polydimethylsiloxane membrane. A set of 254 compounds and their experimentally derived maximum steady state flux values used in this study was gathered from the literature. A total of 42 molecular descriptors were calculated for each compound. A genetic algorithm was used to select important molecular descriptors and supervised ANN was used to correlate selected descriptors with the experimentally derived maximum steady-state flux through the polydimethylsiloxane membrane (log J). Calculated molecular descriptors were used as the ANN's inputs and log J as the output. Developed model indicates that molecular shape and size, inter-molecular interactions, hydrogen-bonding capacity of drugs, and conformational stability could be used to predict drug absorption through skin. A 12-descriptor nonlinear computational neural network model has been developed for the estimation of log J values for a data set of 254 drugs. Described model does not require experimental parameters and could potentially provide useful prediction of membrane penetration of new drugs and reduce the need for actual compound synthesis and flux measurements." @default.
• W2030892568 created "2016-06-24" @default.
• W2030892568 creator A5001061510 @default.
• W2030892568 creator A5029247399 @default.
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• W2030892568 date "2001-09-01" @default.
• W2030892568 modified "2023-10-11" @default.
• W2030892568 title "ANN modeling of the penetration across a polydimethylsiloxane membrane from theoretically derived molecular descriptors" @default.
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• W2030892568 doi "https://doi.org/10.1016/s0731-7085(01)00421-6" @default.
• W2030892568 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/11470201" @default.
• W2030892568 hasPublicationYear "2001" @default.
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