J Chem Inf Model - In silico prediction of chemical acute oral toxicity using multi-classification methods.

Tópicos

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Resumo

Chemical acute oral toxicity is an important end point in drug design and environmental risk assessment. However, it is difficult to determine by experiments, and in silico methods are hence developed as an alternative. In this study, a comprehensive data set containing 12,204 diverse compounds with median lethal dose (LD50) was compiled. These chemicals were classified into four categories, namely categories I, II, III and IV, based on the criterion of the U.S. Environmental Protection Agency (EPA). Then several multiclassification models were developed using five machine learning methods, including support vector machine (SVM), C4.5 decision tree (C4.5), random forest (RF), -nearest neighbor (kNN), and na?ve Bayes (NB) algorithms, along with MACCS and FP4 fingerprints. One-against-one (OAO) and binary tree (BT) strategies were employed for SVM multiclassification. Performances were measured by two external validation sets containing 1678 and 375 chemicals, separately. The overall accuracy of the MACCS-SVM(OAO) model was 83.0% and 89.9% for external validation sets I and II, respectively, which showed reliable predictive accuracy for each class. In addition, some representative substructures responsible for acute oral toxicity were identified using information gain and substructure frequency analysis methods, which might be very helpful for further study to avoid the toxicity.

Resumo Limpo

chemic acut oral toxic import end point drug design environment risk assess howev difficult determin experi silico method henc develop altern studi comprehens data set contain divers compound median lethal dose ld compil chemic classifi four categori name categori ii iii iv base criterion us environment protect agenc epa sever multiclassif model develop use five machin learn method includ support vector machin svm c decis tree c random forest rf nearest neighbor knn nave bay nb algorithm along macc fp fingerprint oneagainston oao binari tree bt strategi employ svm multiclassif perform measur two extern valid set contain chemic separ overal accuraci maccssvmoao model extern valid set ii respect show reliabl predict accuraci class addit repres substructur respons acut oral toxic identifi use inform gain substructur frequenc analysi method might help studi avoid toxic

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