Schizophrenia is a chronic psychotic disorder characterized primarily by cognitive deficits. Drugs and therapies are helpful in managing the symptoms, mostly with long-term compliance. There is a pressing need to design more efficient drugs with fewer adverse effects. Solubility, metabolic stability, toxicity, permeability, and transporter effects are important parameters in the efficacy of drug design, which in turn depend upon different physical and chemical characteristics of drugs. In recent years, there has been growing interest in developing computational tools for the discovery and development of drugs for schizophrenia. Some of these methods use machine learning algorithms to predict the efficacy and side effects of the potential drugs. Other studies have used computer simulations to understand the molecular mechanisms underlying the disease and identify new targets for drug development. Topological indices are numeric quantities linked to the chemical structure of drugs and predict the properties, reactivity, and stability of drugs through the quantitative structure-property relationship (QSPR). This work is aimed at using statistical techniques to link QSPR correlating properties with connectivity indices using linear regression. The QSPR model gives quite a better estimation of the properties of drugs, such as melting point, boiling point, enthalpy, flash point, molar refractivity, refractive index, complexity, molecular weight, and refractivity. Results are validated by comparing actual values to estimated values for the drugs.
© 2023 The Authors. Published by American Chemical Society.