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Open Access Prediction of human intestinal absorption using an artificial neural network

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An artificial neural network model is developed to predict percent human intestinal absorption (%FA) of compounds from their molecular structural parameters. These parameters are the polar molecular surface area (PSA), the fraction of polar molecular surface area (FPSA, polar molecular surface area/molecular surface area), the sum of the net atomic charges of oxygen atoms (QO), the sum of the net atomic charges of nitrogen atoms with net negative atomic charges (QN), the sum of the net atomic charges of hydrogen atoms attached to oxygen or nitrogen atoms (QH), and the number of carboxyls (nCOOH). For a training set of 85 compounds and a test set of 10 compounds, root mean squared errors (RMSE) between experimental %FA values and calculated/predicted %FA values are 8.86% and 14.1%, respectively.

Document Type: Research Article

Affiliations: 1: Department of Pharmacy, Zhejiang University City College, Hangzhou, 310015, P.R. China, Email: 2: First Affiliated Hospital of Zhejiang University, Hangzhou, P.R. China 3: Department of Chemistry, Zhejiang University, Hangzhou, P.R. China 4: College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, P.R. China 5: Ningbo Institute of Technology, Zhejiang University, Ningbo, P.R. China

Publication date: 2005-09-01

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  • Pharmazie is a leading journal in the field of pharmaceutical sciences. As a peer-reviewed scientific journal, Pharmazie is regularly indexed in the relevant databases like Web of science, Journal Citation Reports and many others. The journal is open for submissions from the whole spectrum of pharnaceutical sciences including Pharmaceutical Chemistry, Experimental and Clinical Pharmacology, Drug Analysis, Pharmaceutics, Pharmaceutical Biology, Clinical Pharmacy etc.
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