In Search of Predictive Models for Inhibitors of 5-alpha Reductase 2 Based on the Integration of Bioactivity and Molecular Descriptors Data

5-alpha reductase (5 alpha-reductase) is a microsomal protein that converts testosterone into dihydrotestosterone (DHT). When changes occur in the function of this enzyme, disorders such as pseudohermaphroditism, baldness, benign prostatic hyperplasia and prostate cancer may arise. Currently, there are only two marketed drugs, finasteride and dutasteride, for the therapy of benign prostatic hyperplasia, which have long term side effects, stressing the need for the development of better inhibitors. In the present study, we used a dataset of compounds with known inhibitory activity against 5a-reductase (isozyme 2; 5 alpha-R2) obtained from the ChEMBL database, and employed machine learning methods (random forests and support vector machines) to build classifiers for high-throughput virtual screening campaigns to help prioritise molecules for further analysis. The performance of the classification models was evaluated based on sensitivity, specificity, precision, F-score and accuracy. Our results show that, overall the classification models produced by the two algorithms present similar performance. Furthermore, the classifiers show high performance on the identification and discrimination between potent and weak inhibitors.