Predicting Antibacterial Drugs Properties Using Graph Topological Indices and Machine Learning

Quantitative Structure-Property Relationship (QSPR) modeling is one of the novel ways of predicting the physicochemical properties of a drug through its molecular descriptor (topological index (TI)). This study aims to predict the physical properties of antibacterial drugs by utilizing neighborhood sum degree TI (NTI) and employing two regression models namely, simple linear regression (LR) and support vector regression (SVR) as a machine learning model. In contrast to traditional methods of computing TI, we utilize the simplified molecular-input line-entry system (SMILES) of antibacterial drug compounds to compute the numerical parameters linked to antibacterial drug compounds. To enhance the predictive power of the QSPR model, we employ backward elimination method for LR model while incorporating sequential backward search selection (SBSS) technique for the SVR model. The study demonstrated that the SVR model outperformed the LR model, showcasing the SVR model's ability to handle small datasets effectively. Hyperparameter tuning and the SBSS method were crucial in enhancing the SVR model's performance by iteratively excluding non-informative features, thereby reducing error metrics and optimizing the model. The study identified the predictive capability of NTIs for various properties, including boiling point, melting point, flash point, enthalpy of vaporization, molar refraction, polarization, surface tension, and molar volume. The optimized SVR model showed high prediction accuracy for drugs such as Tobramycin, Gentamicin, Linezolid, Moxifloxacin, and Cilastatin, with predicted values closely matching actual physical properties. The results of our research highlight the significance of integrating advanced machine learning algorithms into drug property prediction leading to the optimization of drug design and discovery process.