Unveiling the Future of Drug Discovery: Navigating Pharmacology’s Digital Revolution

 In Genomics Innovation, Genomics News, Genomics Research

In the quest for faster and more cost-effective drug development, a groundbreaking solution has emerged – the marriage of biology and technology. Welcome to the world of bioinformatic tools in pharmacology. The conventional approach to creating new drugs has often been a long and resource-intensive journey, taking decades and significant financial investments. However, recent advancements have illuminated an alternative path that promises to revolutionize the process.

In this article, we embark on a captivating exploration of the intersection between bioinformatics and pharmacology. With the advent of computer-aided drug design (CADD), the landscape of drug development is undergoing a profound transformation. Imagine a world where drug candidates can be scrutinized, evaluated, and optimized within the realm of virtual reality, eliminating the need for costly physical synthesis and experimentation.

We delve into the two core manifestations of CADD – structure-based drug design (SBDD) and ligand-based drug design (LBDD). These approaches open doors to accelerated drug discovery and optimization, offering a glimpse into the future of pharmaceutical research.

Computer-aided drug design (CADD) refers to the use of computational methods and approaches to discover, develop, and analyze drug and active molecules with similar biochemical properties;  it also focuses on improving bioactive molecules, developing possible therapeutic alternatives, and understanding biological events in the molecular scale (Prieto-Martínez et al., 2019; Sabe et al.,  2021). With the implementation of CADD, there is no need to synthesize the compounds of interest, therefore allowing rapid, reliable, and time-saving chemical and biological assessments that would have taken days and cost a lot if physically done in a laboratory setting (Salmanli et al.,  2021).  The implementation of CADD comes in two forms: structure-based drug design (SBDD) or ligand-based drug design (LBDD)

Structure-based drug design (SBDD) 

Through SBDD, determination of key sites and interactions that are significant to the macromolecular target’s biological function is achieved by assessing its 3D structure; this target is typically an RNA or a protein (Bajad et al., 2021). It is a reiterative process, meaning that it takes place over numerous cycles to assure optimization of the lead molecule prior being sent for clinical trials (Surabhi & Singh, 2018).

Of the entire SBDD methodology, its most notable process is molecular docking. It studies the interaction between the ligand and the target receptor and ranks the potential ligands based on the obtained binding affinities computed through scoring functions (Huang & Zou, 2010). Additionally,  molecular docking predicts the ligand’s preferred binding modes or conformations in the receptor’s binding site through the scoring functions (Macalino et al., 2015; Vázquez et al., 2020).  

Some docking platforms are AutoDock Vina, AutoDock, rDock, and DockoMatic (Bajad et al.,  2021). 

It is reportedly more beneficial to compare and combine results from two or more scoring functions and deicing which ligands performed the best rather than just relying on one specific scoring function (Macalino et al., 2015). 

Ligand-based drug design (LBDD) 

While SBDD provides an insightful approach for well-structured targets, ligand-based drug design (LBDD) offers an alternative route when the 3D structure of the target remains elusive. LBDD harnesses the knowledge of active ligands associated with the target to unravel their structural, physiochemical, and molecular properties linked to biological activity (Macalino et al., 2015).

The quantitative structure-activity relationship (QSAR) technique serves as a cornerstone of LBDD. By correlating the structure and activity of molecules, QSAR models predict the behavior of new compounds based on historical data. (Ye et al., 2022). 

For a reliable QSAR model, the following requirements must be met (Cherkasov et al., 2014;  Melo-Filho et al., 2014):  

  • The database for the bioactivity data should be of sufficient number (at least 20), and these  should be obtained from a common experimental procedure with comparable results;
  • Appropriate selection of compounds; 
  • There should be no autocorrelation of molecular descriptors to the ligands to avoid  overfitting the data; and 
  • Internal or external validation of the created model to determine its applicability and  predictivity

Some QSAR tools are ChemAxon, DRAGON 7.0, Corina Symphony, and MOPAC (Gurung et  al., 2021) 

A Glimpse into Tomorrow: Accelerated Discoveries and Informed Innovations

The integration of bioinformatics and pharmacology fuels a new era of drug discovery, characterized by unparalleled efficiency, speed, and cost-effectiveness. The computational capabilities of these tools allow for the processing of vast datasets, empowering researchers with detailed insights that would have taken weeks or even months to gather within the confines of a physical laboratory.

As we bid adieu to the traditional paradigms of drug development, we find ourselves at the threshold of unparalleled possibilities. The symbiotic relationship between biology and technology holds the potential to redefine medical research, unlocking solutions that were once deemed unattainable. In the forthcoming articles of this series, we delve into the methodologies reshaping the landscape of gut microbiome studies, culminating in a comprehensive understanding of the revolutionary transformations sweeping through the field of life sciences.

In the next article and in the final installment of the series, the methodologies involved in gut microbiome studies are discussed. 

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Gurung, A. B., Ali, M. A., Lee, J., Farah, M. A., & Al-Anazi, K. M. (2021). An Updated Review of  Computer-Aided Drug Design and Its Application to COVID-19. BioMed Research International2021, 1–18. https://doi.org/10.1155/2021/8853056 

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Prieto-Martínez, F. D., López-López, E., Eurídice Juárez-Mercado, K., & Medina-Franco, J. L. (2019).  Computational Drug Design Methods—Current and Future Perspectives. In In Silico Drug Design (pp. 19–44). Elsevier. https://doi.org/10.1016/B978-0-12-816125-8.00002-X 

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Ye, J., Yang, X., & Ma, C. (2022). Ligand-Based Drug Design of Novel Antimicrobials against  Staphylococcus aureus by Targeting Bacterial Transcription. International Journal of  Molecular Sciences, 24(1), 339. https://doi.org/10.3390/ijms24010339

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