Role Of Computational Modeling In Pharmaceutical Drug Design And Analysis

Abstract

Abstract Computational modeling has emerged as a powerful tool in drug discovery, formulation, and analytical research. Molecular docking, quantitative structure–activity relationship (QSAR), and molecular dynamics simulations enable the prediction of physicochemical and pharmacological properties of drug candidates. This paper examines the integration of computational approaches with experimental pharmaceutical analysis for efficient drug design and impurity profiling. By simulating molecular interactions, researchers can predict stability, solubility, and reactivity, reducing the need for extensive laboratory trials. The application of artificial intelligence (AI) and machine learning (ML) in computational chemistry further accelerates lead optimization and toxicity prediction. The study also discusses regulatory considerations and validation of computational models in pharmaceutical R&D environments.

Keywords: Computational Modeling, QSAR, Molecular Docking, AI, Drug Design