Prediction of human intestinal absorption using an artificial neural network.
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 (Q(O)), the sum of the net atomic charges of nitrogen atoms with net negative atomic charges (Q(N)), the sum of the net atomic charges of hydrogen atoms attached to oxygen or nitrogen atoms (Q(H)), and the number of carboxyls (nCOOH). For a training set of 85 compounds anda test set of 10 compounds, root mean squared errors (RMSE) between experimental %FA valuesand calculated/predicted %FA values are 8.86% and 14.1%, respectively.
Fu
X C
XC
Department of Pharmacy, Zhejiang University City College, Hangzhou, PR China. Fuxc@zucc.edu.cn
Chen
C X
CX
Wang
G P
GP
Liang
W Q
WQ
Yu
Q S
QS
D001185:Artificial Intelligence; D055598:Chemical Phenomena; D002626:Chemistry, Pharmaceutical; D002627:Chemistry, Physical; D006801:Humans; D007408:Intestinal Absorption; D016571:Neural Networks, Computer; D004364:Pharmaceutical Preparations / Q000737:chemistry; D011237:Predictive Value of Tests
D004364:Pharmaceutical Preparations