Prediction of UGT-mediated Metabolism Using the Manually Curated MetaQSAR Database

Abstract

Even though glucuronidations are the most frequent metabolic reactions of conjugation, both in quantitative and qualitative terms, they have rather seldom been investigated using computational approaches. To fill this gap, we have used the manually collected MetaQSAR metabolic reaction database to generate two models for the prediction of UGT-mediated metabolism, both based on molecular descriptors and implementing the Random Forest algorithm. The first model predicts the occurrence of the reaction and was internally validated with a Matthew correlation coefficient (MCC) of 0.76 and an area under the ROC curve (AUC) of 0.94, and further externally validated using a test set composed of 120 additional xenobiotics (MCC of 0.70 and AUC of 0.90). The second model distinguishes between O- and N-glucuronidations and was optimized by the random undersampling procedure to improve the predictive accuracy during the internal validation, with the recall measure of the minority class increasing from 0.55 to 0.78.

Figure 1

Model data sets: relative proportions between the two classes undergoing binary classification modeling. Panel e shows the ratio of first-generation substrates (I-GEN SUBs) and second-or more generation substrates (II-GEN SUBs) of the Model 1 data set.

Figure 2

Principal component analysis (a,b) and molecular similarity analysis based on Skelspheres descriptors (c) of the Model 1 data set. The data set is composed of 1400 UGT non-substrates and 792 UGT-substrates comprising 400 first-generation substrates (I-GEN SUBs) and 392 second-or more generation substrates (II-GEN SUBs). See the Supporting Information for a more detailed description of the images.

Figure 3

MCCV performance for Model 1, Model 2, and random undersampled Model 2 based on 100 iterations of model generation and evaluation. The performance for the single class is measured in terms of Precision, Recall, and F1 score, while the overall performance is measured in terms of Matthew Correlation Coefficient (MCC) and Area Under ROC Curve (AUC).

Figure 4

Distribution of the probability measures assigned by the RF algorithm during Model 1 LOO validation. The analysis involves the probabilities assigned only to the retrieved true positives and true negatives.

Figure 5

Analysis of the feature importance in Model 1. Distribution of the number of features (a), distribution of the category importance (b), and distribution of the category importance weighted on the total number of features in each category (c).