The prediction of human oral absorption for diffusion rate-limited drugs based on heuristic method and support vector machine.
Support vector machine (SVM), as a novel machine learning technique, was used for the prediction of the human oral absorption for a large and diverse data set using the five descriptors calculated from the molecular structure alone. The molecular descriptors were selected by heuristic method (HM) implemented in CODESSA. At the same time, in order to show the influence of different molecular descriptors on absorption and to well understand the absorption mechanism, HM was used to build several multivariable linear models using different numbers of molecular descriptors. Both the linear and non-linear model can give satisfactory prediction results: the square of correlation coefficient R(2) was 0.78 and 0.86 for the training set, and 0.70 and 0.73 for the test set respectively. In addition, this paper provides a new and effective method for predicting the absorption of the drugs from their structures and gives some insight into structural features related to the absorption of the drugs.
Liu
H X
HX
Department of Chemistry, Lanzhou University, Lanzhou 730000, P.R. China.
Hu
R J
RJ
Zhang
R S
RS
Yao
X J
XJ
Liu
M C
MC
Hu
Z D
ZD
Fan
B T
BT
D000284:Administration, Oral; D004058:Diffusion; D006801:Humans; D007408:Intestinal Absorption; D010599:Pharmacokinetics; D021281:Quantitative Structure-Activity Relationship