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论文类型：期刊论文

发表时间：2009-04-01

发表刊物：QSAR & COMBINATORIAL SCIENCE

收录刊物：SCIE、Scopus

卷号：28

期号：4

页面范围：396-405

ISSN号：1611-020X

关键字：Androgen receptor; Classification; QSAR; Random forest

摘要：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.