Name
Title | ||
|---|---|---|
ADMET evaluation in drug discovery: 15. Accurate prediction of rat oral acute toxicity using relevance vector machine and consensus modeling. |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1186/s13321-016-0117-7 | J. Cheminformatics |
Keywords | Field | DocType |
Support Vector Machine, Random Forest, Consensus Model, Random Forest Model, Relevance Vector Machine | Acute toxicity,Drug discovery,Biology,In vivo,Median lethal dose,Bioinformatics,Relevance vector machine,In silico,Consensus model | Journal |
Volume | Issue | ISSN |
8 | 1 | 1758-2946 |
Citations | PageRank | References |
2 | 0.40 | 27 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tailong Lei | 1 | 2 | 1.07 |
| Youyong Li | 2 | 238 | 18.54 |
| Yunlong Song | 3 | 9 | 1.57 |
| Dan Li | 4 | 26 | 6.82 |
| Huiyong Sun | 5 | 34 | 6.09 |
| Tingjun Hou | 6 | 427 | 54.50 |