Prediction of acute in vivo toxicity of some amine and amide drugs to rats by multiple linear regression, partial least squares and an artificial neural network.
The oral acute in vivo toxicity of 32 amine and amide drugs was related to their structural-dependent properties. Genetic algorithm-partial least-squares and stepwise variable selection was applied to select of meaningful descriptors. Multiple linear regression (MLR), artificial neural network (ANN) and partial least square (PLS) models were created with selected descriptors. The predictive ability of all three models was evaluated and compared on a set of five drugs, which were not used in modeling steps. Average errors of 0.168, 0.169 and 0.259 were obtained for MLR, ANN and PLS, respectively.
Mahani
Mohamad Khayatzadeh
MK
Faculty of Chemistry, Tarbiat Moalem University, Tehran, Iran.
Chaloosi
Marzieh
M
Maragheh
Mohamad Ghanadi
MG
Khanchi
Ali Reza
AR
Afzali
Daryoush
D
D000208:Acute Disease; D000577:Amides / Q000633:toxicity*; D000588:Amines / Q000633:toxicity*; D000818:Animals; D016014:Linear Models; D016571:Neural Networks, Computer; D051381:Rats; D012984:Software
D000577:Amides; D000588:Amines