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: September 1, 2005

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  • Pharmazie is one of the world's leading pharmaceutical journals. As a peer-reviewed scientific journal, DiePharmazie is regularly indexed in Current Contents/Life Sciences, Excerpta Medica, Analytical Abstracts, International Pharmaceutical Abstracts, Beilstein Current Facts in Chemistry, Chemical Engineering and Biotechnology Abstracts (CEABA) and Science Citation Index.
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