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. Author manuscript; available in PMC: 2018 Mar 9.
Published in final edited form as: J Med Chem. 2016 Jul 22;59(15):7075–7088. doi: 10.1021/acs.jmedchem.5b02038

Table 1.

Summarized Statistical Characteristics of QSAR Models Developed with Balanced Dataseta

model CCR k SE SP coverage
Morgan–RF 0.85 0.71 0.85 0.86 0.62
MACCS–RF 0.83 0.66 0.83 0.83 0.67
AtomPair–SVM 0.81 0.62 0.81 0.81 0.65
AtomPair–GBM 0.81 0.62 0.81 0.81 0.65
Dragon–SVM 0.85 0.70 0.85 0.84 0.69
Dragon–GBM 0.85 0.70 0.85 0.84 0.69
CDK–SVM 0.84 0.69 0.85 0.84 0.77
consensus 0.87 0.74 0.87 0.88 1.00
consensus rigor 0.91 0.81 0.96 0.87 0.38
a

RF, random forest; SVM, support vector machine; GBM, gradient boosting machine; CCR, correct classification rate; k, Cohen’s κ coefficient; SE, sensitivity; SP, specificity. Consensus and consensus rigor models were built by averaging the predicted values from the individual model for each machine learning technique (Morgan–RF, MACCS–RF, AtomPair–SVM, Dragon–SVM, and CDK–SVM).