COMPUTATIONAL DRUG DESIGN: APPLICATIONS IN MODERN PHARMACOLOGY

Abstract

Computational drug design has revolutionized modern pharmacology by accelerating the discovery and development of novel therapeutics. Utilizing advanced computational techniques such as molecular docking, pharmacophore modeling, and quantitative structure-activity relationship (QSAR) analysis, researchers can efficiently predict drug-target interactions, optimize lead compounds, and reduce experimental costs. The integration of artificial intelligence (AI) and machine learning (ML) further enhances predictive modeling, enabling the identification of potential drug candidates with improved efficacy and reduced toxicity. This paper explores key computational approaches, their applications in modern drug discovery, and the challenges associated with their implementation. The role of computational methods in personalized medicine and drug repurposing is also highlighted, demonstrating their significance in shaping the future of pharmacology. Keywords: Computational drug design, molecular docking, pharmacophore modeling, QSAR, artificial intelligence, machine learning, drug discovery, personalized medicine, drug repurposing, pharmacology.