Rapid Communication - Biology & Medicine Case Reports (2023) Volume 7, Issue 4

Computational approaches in medicinal chemistry: From drug discovery to design

Kanda Maria^*

Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark

Corresponding Author:

Kanda Maria
Department of Clinical Medicine
Faculty of Health and Medical Sciences
University of Copenhagen, Copenhagen, Denmark
E-mail: maria.@anda.ac.com

Received: 21-June-2023, Manuscript No. BMCR-23-108827; Editor assigned: 23-June-2023, Pre QC No. BMCR -23-108827 (PQ); Reviewed: 06-July-2023, QC No. BMCR-23-108827; Revised: 08-July-2023, Manuscript No. BMCR -23-108827 (R); Published: 17-July-2023, DOI: 10.35841/ bmcr -7.4.156

Citation: Maria K. Computational approaches in medicinal chemistry: From drug discovery to design. Biol Med Case Rep. 2023;7(4):156

Introduction

Quantitative Structure-Activity Relationship (QSAR)

QSAR models establish relationships between the chemical structures of compounds and their biological activities. Using statistical and machine learning methods, QSAR models predict the activity of new compounds based on their physicochemical properties, facilitating the prioritization of molecules for synthesis and testing. Pharmacophore Modeling: Pharmacophore models represent the essential features and spatial arrangement necessary for a ligand to bind to its target. These models aid in virtual screening of compound libraries to identify molecules with similar pharmacophoric properties, allowing the discovery of structurally diverse hits [1].

Virtual Screening

Virtual screening involves the computational screening of large compound libraries against a target protein to identify potential lead compounds. Structure-based virtual screening utilizes molecular docking or other techniques to prioritize compounds based on their binding affinity, while ligandbased virtual screening relies on similarity searching or QSAR models to identify compounds with similar properties to known active compounds. Molecular Dynamics Simulations: Molecular Dynamics (MD) simulations simulate the movement of atoms and molecules over time. In medicinal chemistry, MD simulations can provide insights into the behavior of drug molecules in the target environment, including protein-ligand interactions, conformational changes, and binding kinetics. MD simulations can guide lead optimization and provide information about drug stability [2].

De Novo Drug Design

De novo drug design involves the generation of entirely new compounds using computational methods. By exploring chemical space and optimizing properties such as binding affinity and drug-likeness, de novo design algorithms generate novel molecules with desired characteristics, offering opportunities for lead discovery. Machine Learning and Artificial Intelligence: Machine learning and artificial intelligence techniques are employed to analyze large datasets, predict properties, and guide decision-making in medicinal chemistry. These methods can identify patterns and relationships in chemical and biological data, aiding in compound selection, toxicity prediction, and optimization of drug candidates [3].

These computational approaches, often used in combination, have revolutionized medicinal chemistry by accelerating the drug discovery process, reducing costs, and increasing the success rate of lead identification and optimization. They enable researchers to explore a vast chemical space and prioritize compounds with the greatest potential for therapeutic applications. Medicinal chemistry is the intersection of chemistry, pharmacology, and biology, with the goal of discovering and designing new drugs that can effectively treat diseases. Computational approaches have become an increasingly important tool in this field, as they allow researchers to accelerate the drug discovery and design process by predicting how a drug will interact with a target protein and identifying compounds with potential therapeutic properties [4].

Molecular Docking

This method predicts the binding affinity of a small molecule to a protein target by simulating the formation of a complex between the two. The binding affinity can be used to estimate the potency of the compound as a drug.

Quantitative Structure-Activity Relationship (QSAR): QSAR models use statistical methods to correlate the chemical structure of a compound with its biological activity. These models can be used to predict the activity of new compounds, guide the synthesis of new compounds with desired properties, and optimize existing drugs. Molecular Dynamics: Molecular dynamics simulations use computer algorithms to model the behavior of molecules over time, allowing researchers to study how a drug interacts with a target protein on a molecular level.

Machine learning: Machine learning algorithms can be used to analyze large datasets of chemical and biological data, including high-throughput screening data, to identify potential drug candidates and predict their activity.

Virtual screening: Virtual screening involves the use of computational methods to search large databases of compounds for those that are likely to bind to a target protein. This approach can help researchers identify new drug candidates more quickly and cost-effectively than traditional methods.

Structure-based drug design: Structure-based drug design involves using the three-dimensional structure of a target protein to design compounds that will bind to it with high affinity and specificity [5].

Conclusion

In summary, computational approaches are valuable tools in medicinal chemistry for accelerating the drug discovery and design process, optimizing existing drugs, and identifying potential drug candidates. By combining computational methods with experimental techniques, researchers can develop new drugs that are more effective, safer, and have fewer side effects.

References

1. Menikarachchi LC, Gascon JA. QM/MM approaches in medicinal chemistry research. Curr Top Med Chem. 2010;10(1):46-54.

Indexed at, Google Scholar, Cross Ref

2. Maggiora G, Vogt M, Stumpfe D, et al. Molecular similarity in medicinal chemistry: Mini perspective. J Med Chem. 2014;57(8):3186-204.

   Google Scholar

3. Tutone M, Almerico AM. Computational approaches: Drug discovery and design in medicinal chemistry and bioinformatics. Mol. 2021;26(24):7500.

Indexed at, Google Scholar, Cross Ref

4. Bajorath J. Pushing the boundaries of computational approaches: special focus issue on computational chemistry and computer-aided drug discovery. Future Med Chem. 2015;7(18):2415-7.

Indexed at, Google Scholar, Cross Ref

5. Lipinski CA, Lombardo F, Dominy BW, et al. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev. 2012;64:4-17.

 

Indexed at, Google Scholar, Cross Ref