On QSPR analysis of glaucoma drugs using machine learning with XGBoost and regression models

Glaucoma is an irreversible, progressive, degenerative eye disorder arising because of increased intraocular pressure, resulting in eventual vision loss if untreated. The QSPR relates, mathematically, by employing various algorithms, a specified property of a molecule that arises either from physical, chemical, or biological phenomena using various aspects of its structure. Here in, a similar application based on topological indices and inferences derived from the structure for the calculation of different drug properties like molar refractivity, refractive index, enthalpy, boiling points, molecular weight, and polarizability is presented. Linear regression is developed between the features of QSPR, coupled with topological indices, and performance assessment is conducted in conjunction with an Extreme Gradient Boosting model. From the obtained results, one can draw out that the XGBoost model will give better results than those from simple regression methods, specifically for polarizability. Predicted values validate the experiments. This work shows how the combination of machine learning techniques and topological indices can be used to get the best out of predictive modeling.