Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset
Abstract
:1. Introduction
2. Results
2.1. Dataset Analysis
2.2. Performances of Models
2.3. y-Randomization Test
2.4. Descriptors Associated with Hepatotoxicity
2.5. Virtual Screening
2.6. Outliers, Applicability Domain and Wrongly Classified Drugs
3. Discussion
4. Materials and Methods
4.1. Dataset
4.2. Descriptors
4.3. Feature Selection
4.4. Classification Algorithms
4.5. Performance Evaluation
4.6. Virtual Screening
- (a)
- By a majority vote applied to the classification performed by each model;
- (b)
- By computing the average of the probabilities outputted by each model and then applying the 50% threshold to classify the compound as being of concern or of no concern (only 72 models outputted probabilities, 6 only made binary predictions);
- (c)
- By developing meta-models using the predictions of the best 50 models (selected with the help of the same selection algorithms as for the building of the individual models) as independent variables for the final classification. We evaluated models based exclusively on the 50 best-performing individual models. We also built models that additionally included the dose and duration of treatment as supplementary features for the improvement of the performance.
4.7. Outliers and Applicability Domain
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
Abbreviations
AD | Applicability Domain |
ANNs | Artificial Neural Networks |
AUC | Area Under the Receiver Operating Characteristics Curve |
BA | Balanced Accuracy |
BART | Bayesian Additive Regression Trees |
CV | Cross-Validation |
DC-SVM | Divide-and-Conquer SVM |
DILI | Drug induced liver injury |
ECHA | European Chemicals Agency |
ERT | Extremely Randomized Trees |
FPR | False Positive Rate |
IBk | an RWeka implementation of the knn algorithm |
IFOREST | Isolation Forest |
knn | k-nearest neighbours |
LDA | Linear Discriminant Analysis |
MMCE | Mean MisClassification Error |
NIDDK | National Institute of Diabetes and Digestive and Kidney Diseases |
OECD | The Organisation for Economic Co-operation and Development |
PPV | Positive Predictive Value |
QSAR | Quantitative Structure–Activity Relationship |
RDA | Regularized Discriminant Analysis |
RF | Random Forests |
SOD | Subspace Outlier Detection |
SVM | Support Vector Machines |
TNR | True Negative Rate |
TPR | True Positive Rate |
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Descriptor | Interpretation | Descriptor Block (group) | Frequency Occurring Among the First 5 Most Important Features | Sense of the Contribution * |
---|---|---|---|---|
Mp | mean atomic polarizability (scaled on Carbon atom) | Constitutional indices | 12 (70.59%) | + |
H% | percentage of H atoms | Constitutional indices | 12 (70.59%) | − |
GATS1m | Geary autocorrelation of lag 1 weighted by mass | 2D autocorrelations | 12 (70.59%) | − |
SpPosA_B(m) | normalized spectral positive sum from Burden matrix weighted by mass | 2D matrix-based descriptors | 10 (58.82%) | + |
MLOGP | Moriguchi octanol-water partition coeff. (logP) | Molecular properties | 4 (23.53%) | + |
PCR | ratio of multiple path count over path count | Walk and path counts | 3 (17.65%) | + |
totalcharge | total charge | Constitutional indices | 2 (11.76%) | − |
SM1_Dz.m. | spectral moment of order 1 from Barysz matrix weighted by mass | 2D matrix-based descriptors | 2 (11.76%) | + |
SIC1 | Structural Information Content index (neighborhood symmetry of 1-order) | Information indices | 2 (11.76%) | + |
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Ancuceanu, R.; Hovanet, M.V.; Anghel, A.I.; Furtunescu, F.; Neagu, M.; Constantin, C.; Dinu, M. Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset. Int. J. Mol. Sci. 2020, 21, 2114. https://doi.org/10.3390/ijms21062114
Ancuceanu R, Hovanet MV, Anghel AI, Furtunescu F, Neagu M, Constantin C, Dinu M. Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset. International Journal of Molecular Sciences. 2020; 21(6):2114. https://doi.org/10.3390/ijms21062114
Chicago/Turabian StyleAncuceanu, Robert, Marilena Viorica Hovanet, Adriana Iuliana Anghel, Florentina Furtunescu, Monica Neagu, Carolina Constantin, and Mihaela Dinu. 2020. "Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset" International Journal of Molecular Sciences 21, no. 6: 2114. https://doi.org/10.3390/ijms21062114