Tailong Lei1, Youyong Li2, Yunlong Song3, Dan Li1, Huiyong Sun1, Tingjun Hou4. 1. College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058 Zhejiang People's Republic of China. 2. Institute of Functional Nano and Soft Materials (FUNSOM), Soochow University, Suzhou, 215123 Jiangsu People's Republic of China. 3. Department of Medicinal Chemistry, School of Pharmacy, Second Military Medical University, Shanghai, 200433 People's Republic of China. 4. College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058 Zhejiang People's Republic of China ; State Key Lab of CAD&CG, Zhejiang University, Hangzhou, 310058 Zhejiang People's Republic of China.
Abstract
BACKGROUND: 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. RESULTS: 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. CONCLUSION: Considering the overall prediction accuracy for the test set, RVM with Laplacian kernel and RF were recommended to build in silico models with better predictivity for rat oral acute toxicity. By combining the predictions from individual models, four consensus models were developed, yielding better prediction capabilities for the test set (q ext (2) = 0.669-0.689). Finally, some essential descriptors and substructures relevant to oral acute toxicity were identified and analyzed, and they may be served as property or substructure alerts to avoid toxicity. We believe that the best consensus model with high prediction accuracy can be used as a reliable virtual screening tool to filter out compounds with high rat oral acute toxicity. Graphical abstractWorkflow of combinatorial QSAR modelling to predict rat oral acute toxicity.
BACKGROUND: 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. RESULTS: 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. CONCLUSION: Considering the overall prediction accuracy for the test set, RVM with Laplacian kernel and RF were recommended to build in silico models with better predictivity for ratoral acute toxicity. By combining the predictions from individual models, four consensus models were developed, yielding better prediction capabilities for the test set (q ext (2) = 0.669-0.689). Finally, some essential descriptors and substructures relevant to oral acute toxicity were identified and analyzed, and they may be served as property or substructure alerts to avoid toxicity. We believe that the best consensus model with high prediction accuracy can be used as a reliable virtual screening tool to filter out compounds with high ratoral acute toxicity. Graphical abstractWorkflow of combinatorial QSAR modelling to predict ratoral acute toxicity.