ADMET evaluation in drug discovery: 15. Accurate prediction of rat oral acute toxicity using relevance vector machine and consensus modeling

Journal of Cheminformatics
Tailong LeiTingjun Hou

Abstract

Determination of acute toxicity, expressed as median lethal dose (LD50), is one of the most important steps in drug discovery pipeline. Because in vivo assays for oral acute toxicity in mammals are time-consuming and costly, there is thus an urgent need to develop in silico prediction models of oral acute toxicity. In this study, based on a comprehensive data set containing 7314 diverse chemicals with rat oral LD50 values, relevance vector machine (RVM) technique was employed to build the regression models for the prediction of oral acute toxicity in rate, which were compared with those built using other six machine learning approaches, including k-nearest-neighbor regression, random forest (RF), support vector machine, local approximate Gaussian process, multilayer perceptron ensemble, and eXtreme gradient boosting. A subset of the original molecular descriptors and structural fingerprints (PubChem or SubFP) was chosen by the Chi squared statistics. The prediction capabilities of individual QSAR models, measured by q ext (2) for the test set containing 2376 molecules, ranged from 0.572 to 0.659. Considering the overall prediction accuracy for the test set, RVM with Laplacian kernel and RF were recommended to build in silico mo...Continue Reading

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Citations

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Methods Mentioned

BETA
in silico methods
PCA

Software Mentioned

R
RVM
XGBoost
PaDEL
Discovery Studio
eXtreme
Pipeline Pilot
rminer
SMILES
MOE

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