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Letters in Organic Chemistry

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ISSN (Print): 1570-1786
ISSN (Online): 1875-6255

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Review Article

Harnessing Computational Modeling for Efficient Drug Design Strategies

Author(s): Kuldeep Singh*, Bharat Bhushan, Akhalesh Kumar Dube, Anit Kumar Jha, Ketki Rani, Akhilesh Kumar Mishra and Prateek Porwal

Volume 21, Issue 6, 2024

Published on: 16 January, 2024

Page: [479 - 492] Pages: 14

DOI: 10.2174/0115701786267754231114064015

Price: $65

Abstract

Computational modeling has become a crucial tool in drug design, offering efficiency and cost-effectiveness. This paper discusses the various computational modeling techniques used in drug design and their role in enabling efficient drug discovery strategies. Molecular docking predicts the binding affinity of a small molecule to a target protein, allowing the researchers to identify potential lead compounds and optimize their interactions. Molecular dynamics simulations provide insights into protein-ligand complexes, enabling the exploration of conformational changes, binding free energies, and fundamental protein-ligand interactions. Integrating computational modeling with machine learning algorithms, such as QSAR modeling and virtual screening, enables the prediction of compound properties and prioritizes potential drug candidates. High-performance computing resources and advanced algorithms are essential for accelerating drug design workflows, with parallel computing, cloud computing, and GPU acceleration reducing computational time. The paper also addresses the challenges and limitations of computational modeling in drug design, such as the accuracy of scoring functions, protein flexibility representation, and validation of predictive models. It emphasizes the need for experimental validation and iterative refinement of computational predictions to ensure the reliability and efficacy of designed drugs.

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