Discrimination of approved drugs from experimental drugs by learning methods.

Background: To assess whether a compound is druglike or not as early as possible is always critical in drug discovery process. There have been many efforts made to create sets of 'rules' or 'filters' which, it is hoped, will help chemists to identify 'drug-like' molecules from 'non-drug' molecules. However, among the chemical space of the druglike molecules, the minority will be approved drugs. Classifying approved drugs from experimental drugs may be more helpful to obtain future approved drugs. Therefore, discrimination of approved drugs from experimental ones has been done in this paper by analyzing the compounds in terms of existing drugs features and machine learning methods.
Results: Four methodologies were compared by their performance to classify approved drugs from experimental ones. The best results were obtained by SVM, in which the accuracy is 0.7911, the sensitivity is 0.5929, and the specificity is 0.8743. Based on the results, consensus model was developed to effectively discriminate drugs, which further pushed the correct classification rate up to 0.8517, sensitivity up to 0.7242, specificity up to 0.9352. The applications on the Traditional Chinese Medicine Ingredients Database (TCM-ID) tested the methods. Therefore this model has been proven to be a potent tool for identifying drug molecules.
Conclusion: The studies would have potential applications in the research of combinatorial library design and virtual high throughput screening for drug discovery.