Analysis of structure-toxicity relationships for a series of amide herbicides using statistical methods and neural network.
Structure-toxicity relationships were studied for a set of 44 herbicides by means of principal component analysis (PCA), multiple regression analysis (MRA), and neural network (NN). The values of log LD50 (lethal dose 50, acute, oral, rat) of the studied compounds were well correlated with the descriptors encoding the chemical structures. Considering the pertinent descriptors, a correlation coefficient of 0.90 (n = 41) was obtained for the NN model with a configuration of 4-3-1 (and 0.92 (n = 41) with a configuration of 4-5-1). To evaluate the contribution of each descriptor on the activity, log LD50 was calculated by removing each descriptor a part. This approach provides the tendency of a descriptor to be favourable (or not) to the activity.
Zakarya
D
D
Faculté des Sciences et Techniques, B.P. 146, Mohammadia, Morocco.
Larfaoui
E M
EM
Boulaamail
A
A
Lakhlifi
T
T
D000577:Amides / Q000737:chemistry / Q000633:toxicity*; D000818:Animals; D055598:Chemical Phenomena; D002627:Chemistry, Physical; D006540:Herbicides / Q000737:chemistry / Q000633:toxicity*; D007928:Lethal Dose 50; D015999:Multivariate Analysis; D016571:Neural Networks, Computer; D051381:Rats; D012044:Regression Analysis; D013329:Structure-Activity Relationship
D000577:Amides; D006540:Herbicides