In silico Prediction of Androgenic and Nonandrogenic Compounds Using Random Forest

Abstract The purpose of the present study was to develop in silico models allowing for a reliable prediction of androgenic and nonandrogenic compounds based on a large diverse dataset of 205 compounds. As a new classification method, the Random Forest (RF) was applied, its performance to classify these compounds in terms of their Quantitative Structure–Activity Relationships (QSAR) was evaluated and also compared with the widely used Partial Least Squares (PLS) analysis for the dataset. The predictive power of these methods was verified with five‐fold cross‐validation and an independent test set. For the RF model, the prediction accuracies of the androgenic and nonandrogenic compounds are 81.0 and 77.0% for cross‐validation, respectively, averaging 87.3% of correctly classified compounds in the external tests. The PLS is slightly weak, showing an average prediction accuracy of 75 and 74.7% for the cross‐validation and external validation, respectively. Our analysis demonstrates that RF is a powerful tool capable of building models for the data and should be valuable for virtual screening of androgen receptor‐binding ligands.