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Sensitivity (SENS)
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TP/(TP + FN)
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Measures the ability of a QSAR tool to detect Ames positives compounds correctly.
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Specificity (SPEC)
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TN/(TN + FN)
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Measures the ability for a QSAR tool to detect negatives compounds.
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Accuracy (ACC)
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(TP + TN)/(TP + TN + FP + FN)
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Assesses a QSAR tool’s overall performance by returning the fraction of compounds which were correctly predicted.
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Balanced Accuracy (BA)
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(SENS + SPEC)/2
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Assesses the overall model performance, giving each class equal weight.
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Positive Prediction Value (PPV)
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(TP)/(TP + FP)
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Indicates how frequently positive predictions are correct.
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Negative Prediction Value (NPV)
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TN/(TN + FN)
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Indicates how often negative predictions are correct.
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Mathews Correlation Coefficient (MCC)
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\( \frac{\left(\left( TP\ast TN\right)-\left( FP\ast FN\right)\right)}{\sqrt{\left( TP+ FP\left)\left( TP+ FN\right)\left( TN+ FP\right)\right( TN+ FN\right)}} \)
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Assesses the overall performance of the model. Values can range from −1 to 1, which is in contrast to the other metrics in this table which range form 0 to 1.
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Coverage (COV)
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(TP + TN + FP + FN)/(TP + TN + FP + FN + OOD)
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Evaluates the proportion of compounds for which the model can make a positive or negative prediction.
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