Integrating Structural Modeling and Decision-Making for Anti-psychotic Drugs through Topological Indices

Psychosis encompasses a spectrum of mental disorders characterized by abnormalities in five key domains: delusions, hallucinations, disorganized thinking (speech), grossly disorganized or abnormal motor behavior (including catatonia), and negative symptoms. Contemporary research on developing new antipsychotic drugs addresses for improving efficacy, reducing side effects, and better management of symptoms. The physicochemical properties of drugs play a pivotal role in their efficacy, influencing factors such as absorption, distribution, metabolism, and excretion within the body. Our work describes the potential role of topological indices in quantitative structure-property relationship (QSPR) modeling of antipsychotic drugs as well as in multiple-choice decision-making. QSPR modeling is employed to assess the physiochemical properties using degree-based topological indices. Seventeen antipsychotic drugs are analyzed for various physicochemical characteristics, and a QSPR model is built using 13 topological indices. The analysis yields insights into the complexity, molar refractivity, flash point, molar volume, and other relevant factors of the drugs. The drugs are ranked on the basis of correlation of the topological indices and the properties. Furthermore, rankings obtained through both Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and simple additive weighting (SAW) methods depict a remarkable level of agreement, enhancing the credibility of the assessment methodologies. These findings highlight the efficacy of topological indices in accurately representing the theoretical features of antipsychotic drugs and their significant correlation with physical attributes, thereby informing decision-making in drug development and clinical practice.