Research Paper Volume 14, Issue 3 pp 1448—1472
Screening of antibacterial compounds with novel structure from the FDA approved drugs using machine learning methods
Figure 1. The prediction accuracy of different machine learning methods for benchmark datasets. The filtered datasets include one positive dataset and 10 negative datasets, therefore, each value in the figure is the average of 10 prediction accuracy. Compared with other machine learning methods, random forest (RF), support vector machine (SVM), and multi-layer perception (MLP) all show higher prediction accuracy. The benchmark dataset based on FP2 molecular fingerprints shows the highest prediction accuracy in the RF and MLP methods, and also shows high prediction accuracy in the SVM method among all molecular fingerprints. The accuracy fluctuates greatly among different machine learning methods in the benchmark dataset based on vector features.