Ensemble Models Based on QuBiLS-MAS Features and Shallow Learning for the Prediction of Drug-Induced Liver Toxicity: Improving Deep Learning and Traditional Approaches

计算毒理学

Drug-induced liver injury (DILI) is a key safety issue in the drug discovery pipeline and a regulatory concern. Thus, many in silico tools have been proposed to improve the hepatotoxicity prediction o

NIAID Data Ecosystem30

DataSheet1_Estimating provisional margins of exposure for data-poor chemicals using high-throughput computational methods.ZIP

化学品风险评估

计算毒理学

Current computational technologies hold promise for prioritizing the testing of the thousands of chemicals in commerce. Here, a case study is presented demonstrating comparative risk-prioritization ap

NIAID Data Ecosystem10

An Evaluation of Existing QSAR Models and Structural Alerts and Development of New Ensemble Models for Genotoxicity Using a Newly Compiled Experimental Dataset

遗传毒性预测

QSAR模型评估

Data files supporting manuscript Pradeep P, Judson R, DeMarini DM, Keshava N, Martin TM, Dean J, Gibbons CF, Simha A, Warren SH, Gwinn MR, Patlewicz G. 2021. An Evaluation of Existing QSAR Models and

Zenodo2021-03-26 更新00

An Evaluation of Existing QSAR Models and Structural Alerts and Development of New Ensemble Models for Genotoxicity Using a Newly Compiled Experimental Dataset

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