Computational Molecular Biology 2024, Vol.14, No.3, 115-124 http://bioscipublisher.com/index.php/cmb 115 Feature Review Open Access Exploration of the Role of Computational Chemistry in Modern Drug Discovery Xaiohua Zhang, Jianhui Li Institute of Life Sciences, Jiyang Colloge of Zhejiang A&F University, Zhuji, 311800, Zhejiang, China Corresponding author: jianhui li@jicat.org Computational Molecular Biology, 2024, Vol.14, No.3 doi: 10.5376/cmb.2024.14.0014 Received: 09 Apr., 2024 Accepted: 24 May, 2024 Published: 13 Jun., 2024 Copyright © 2024 Zhang and Li, This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Preferred citation for this article: Zhang X.H., and Li J.H., 2024, Exploration of the role of computational chemistry in modern drug discovery, Computational Molecular Biology, 14(3): 115-124 (doi: 10.5376/cmb.2024.14.0014) Abstract This study explores the fundamental principles of computational chemistry, such as quantum mechanics and molecular modeling, and investigates their applications in drug design, including structure based and ligand based methods. Emphasis was placed on the integration of advanced technologies such as machine learning and high-throughput virtual screening, highlighting their role in improving prediction accuracy and accelerating drug development. However, challenges such as prediction reliability, computational cost, and integration of computational data with experimental results still exist. The case study demonstrated the effectiveness of the computational method and compared it with traditional methods in developing successful candidate drugs. Looking to the future, the potential of combining computational chemistry and omics data and their role in advancing personalized medicine. Future drug discovery is likely to rely on collaborative platforms and open-source tools to push the boundaries of computational innovation. Keywords Computational chemistry; Drug discovery; Molecular modeling; Machine learning; Structure-based design 1 Introduction Computational chemistry has become an indispensable tool in the field of drug discovery, offering innovative solutions to complex problems and significantly reducing the time and cost associated with bringing new drugs to market. Computational chemistry encompasses a wide range of techniques and methodologies that leverage computer simulations to solve chemical problems. These techniques include molecular dynamics, quantum chemistry, machine learning, and various other modeling approaches. The integration of these methods allows researchers to predict chemical properties, optimize drug candidates, and understand molecular interactions at an unprecedented level of detail (Cova and Pais, 2019; Decherchi and Cavalli, 2020). The synergy between computational tools and experimental methods has led to significant advancements in the field, enabling the design and evaluation of new drugs with greater efficiency and accuracy (Rosales-Hernández and Correa-Basurto, 2015; Castelli et al., 2021). The application of computational methods in drug discovery dates back over three decades, with early efforts focusing on structure-based and ligand-based approaches (Sliwoski et al., 2014). Initially, these methods were limited by computational power and the availability of biological data. However, advancements in hardware, algorithms, and the accumulation of biological and chemical data have transformed computational chemistry into a cornerstone of pharmaceutical research (Abramov et al., 2022; Blunt et al., 2022). The human genome project and the increasing knowledge of biological structures have further propelled the use of in silico tools, making them integral to various phases of the drug discovery pipeline (Cui et al., 2020). This study provides a comprehensive overview of the current status of computational chemistry in drug discovery, investigating the latest methods, applications, and trends. We will discuss the challenges faced by researchers, such as the complexity of biological systems and the need for accurate predictive models. In addition, we will explore the prospects of emerging technologies, including quantum computing and machine learning, and their revolutionary potential for this field. Successful case studies emphasize the crucial role of computational chemistry in future drug discovery.
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