review · Discover Public Health
This review examines current trends in computational approaches for drug design and metabolism. It highlights the essential role of computer-aided drug design and discovery methods in developing therapeutic small molecules. The methodology involved a thorough literature search across multiple databases to identify relevant studies. The review provides insights into various in silico and ab initio methods and algorithms, including practical applications of artificial intelligence in drug discovery. Specific computational tools such as molecular dynamics, molecular docking, quantum mechanics, hybrid quantum mechanics/molecular mechanics, and density functional theory are discussed. It also covers ligand-based and structure-based drug discoveries, force field models, docking algorithms, and QM/MM coupling. The paper concludes that these evolving computational approaches are crucial for overcoming challenges in developing medicines for complex diseases and will aid in discovering novel compounds with high therapeutic performance.
This research is important because it summarises how advanced computer methods are speeding up the discovery of new medicines. By using computational tools, scientists can more efficiently identify potential drug candidates and understand how they work, ultimately leading to better treatments for various diseases and contributing to global health goals.
This review focuses on early-stage research and development, providing a comprehensive overview of computational tools used in drug discovery, target identification, hit discovery, and lead optimisation. Potential users include pharmaceutical companies, biotechnology firms, and academic research institutions involved in drug development. The insights could inform the adoption and further development of computational platforms to accelerate the identification and optimisation of novel therapeutic compounds, moving towards the discovery of new drugs with high therapeutic performance.
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Computer-aided drug design and discovery methods have been essential in developing small molecules with therapeutic properties over the last decades. Application of computational resources includes drug target identification, hit discovery, and lead optimization. Accordingly, with tremendous research efforts and the availability of financial support from government agencies across the world, and multinational drug companies, the overall research level in this area will continue to advance. The methodology used in this review paper entailed a thorough examination of research studies on relevant literature on drug design and development using computational resources. Extensive searches using Scopus, International Pharmaceutical Abstracts (OvidSp, WHO Global Health Library, Cochrane, Google Scholar, Web of Science, Science Direct, ProQuest dissertation & theses, Worldwide Political Science Abstracts (CSA), and PubMed was carried out. A standardized template was used to ensure that the selected papers met the inclusion criteria, and relevant to the review. Ultimately, there are robust technologies developed to enhance the drug discovery process. Therefore, this review provides insights into computational resources in Silico and ab initio methods and algorithms, not restricted to drug metabolism predictions for drug design, and the practical applications of artificial intelligence (AI) in drug discovery. Computational tools and methods for drug design and development such as molecular dynamics (MD), molecular docking, quantum mechanics (QM), hybrid quantum mechanics/molecular mechanics (QM/MM), and Density functional theory (DFT) have been reviewed. Accordingly, the emerging technique of synergistically employing these techniques influences the fundamental challenges of conventional medicines for complex diseases. Herein, we discuss ligand-based and structure-based drug discoveries, force field models in MD simulations, docking algorithms, subtractive and additive QM/MM coupling. Nonetheless, as computer-aided drug (CADD) approaches continue to evolve with significant improvements, the focus areas will be on docking and virtual screening, scoring functions, optimization of hits, and assessment of adsorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. With the current success, the present computational resources will aid in the future discovery of novel compounds with high therapeutic performance. The ongoing oncology research efforts will also significantly contribute to UN sustainable development goals – good health and well-being, sustainable innovation and industrialization.
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DOI: 10.1186/s12982-024-00229-3
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