Integrative QSPR and VIKOR Multi-Criteria Decision Analysis for Optimizing Anti-Parkinson Drug Candidates

Abstract

Developing efficient anti-Parkinson medications poses a considerable challenge in the field of pharmacology, necessitating sophisticated techniques for assessing and refining potential therapeutic agents. This research presents a unified method that merges Quantitative Structure-Property Relationship (QSPR) analysis with VIKOR Multi-Criteria decision-making (MCDM) to enhance the selection and refinement of anti-Parkinson drug candidates. QSPR analysis aims to elucidate the connection between molecular descriptors and the pharmacological characteristics of different anti-Parkinson compounds. By pinpointing essential molecular elements that influence both drug efficacy and safety, QSPR models yield predictive insights that direct the design and choice of new drug candidates. Subsequently, the VIKOR method is utilized to prioritize and choose the most promising drug candidates according to their anticipated performance. This method incorporates a range of pharmacological and safety considerations, enabling a balanced evaluation that weighs therapeutic advantages against potential risks. The collaborative QSPR-VIKOR approach facilitates a thorough assessment of drug candidates, reconciling conflicting goals and offering a definitive ranking system for decision-making. By integrating the benefits of both strategies, this study seeks to identify ideal anti-Parkinson drug candidates with improved efficacy and safety profiles. The results offer a solid groundwork for the systematic assessment and enhancement of new therapeutic agents, potentially hastening the creation of more effective treatments for Parkinson’s disease and enhancing patient outcomes and their quality of life.