Screening of Antibacterial Compounds With Novel Structure From The FDA Approved Drugs Using Machine Learning Methods

Background: Due to the lack of new antibiotics in recent years, bacterial resistance has increasingly become a serious problem globally. The aim of this study is to construct an antibacterial compound predictor using machine learning methods to screen potential antibacterial drugs.Methods: Active and inactive antibacterial compounds were acquired from the ChEMBL and PubChem database, which were used to construct benchmark datasets. The antibacterial compound predictor is constructed using the support vector machine (SVM), random forest (RF), and multi-layer perception (MLP) methods. We predicted the antibacterial activity of the Food and Drug Administration (FDA) approved drugs in the DrugBank database and screened novel antibacterial drugs through structural similarity analysis.Results: In the initial screen process, the results suggested that the benchmark dataset based on FP2 molecular fingerprints, along with the SVM, RF, and MLP methods showed excellent prediction performance (mean AUC > 0.9 for all models). Using the combination of these three models, a total of 957 potential antibacterial drugs were predicted. Most of the predicted drugs showed low structural similarities compared with the FDA approved antibacterial drugs. We finally screened 9 predicted antibacterial drugs with novel structures including 2 anti-tumor drugs (cyclophosphamide and ifosfamide), 2 ophthalmic drugs (apraclonidine and echothiophate) and 5 anesthetics (desflurane, enflurane, isoflurane, methoxyflurane, and sevoflurane).Conclusions: This study provides a new insight for predicting antibacterial compounds with novel structures by using FDA approved drugs. The predicted compounds with novel structures are worthy of further experimental verification in the future.