COMPUTATIONAL CHEMISTRY IN DRUG DISCOVERY: INSIGHTS AND INNOVATIONS - A REVIEW

Abstract

Computational chemistry has significantly advanced the development of therapeutically effective small molecules. This review highlights major applications such as molecular modeling, structure-based drug design (SBDD), quantitative structure-activity relationship (QSAR) analysis, pharmacokinetics/pharmacodynamics (PK/PD) modeling, de novo drug development, repurposing, and toxicity prediction. By integrating computational and experimental methods, we gain a deeper understanding of drug-target interactions. The review offers a comprehensive analysis of each technique, drawing from recent research to demonstrate how computational chemistry is revolutionizing drug discovery and development, particularly in enhancing efficiency and reducing the cost and time of traditional approaches.

Objectives

This review explores the diverse applications of computational chemistry in drug design, emphasizing its role in accelerating therapeutic innovation while reducing costs and late-stage failure rates. By improving our understanding of ligand-protein interactions, computational methods have become essential in modern drug discovery pipelines.

Result

Our analysis demonstrates that computational chemistry enhances drug development through:

• Detailed molecular modeling and simulation for optimizing drug-target interactions. 
• Effective lead identification using SBDD, including molecular docking and pharmacophore modeling.
• QSAR modeling to predict biological activity based on chemical structure.
• PK/PD modeling to optimize dosing and ensure drug efficacy and safety.
• De novo design of novel drug molecules using generative models.
• Integrating artificial intelligence to accelerate innovation and improve accuracy in drug discovery.

Conclusion

Computational chemistry has transformed the pharmaceutical industry by enabling faster, more accurate, and cost-effective drug design. Techniques such as molecular docking, pharmacophore modeling, and virtual screening streamline the discovery process. The integration of artificial intelligence further elevates drug development, paving the way for innovative therapies that address complex medical challenges more effectively.