Abstract

It takes a long time and a lot of effort to discover and develop new drugs, which necessitates extensive study and testing. With the help of computational techniques and data analysis, bioinformatics has grown to be a potent tool for drug discovery in recent years, allowing researchers to find new drugs faster. In this review, we examine the role of bioinformatics in drug discovery, including the use of ligand- and structure-based drug design, virtual screening based on pharmacophore models, de novo design based on pharmacophore models, and quantitative structure-activity relationship (QSAR) models and machine learning techniques. We also talk about how important data collection from different sources, like natural and synthetic databases, is for supporting drug discovery efforts. We highlight the potential of bioinformatics to revolutionise the field of drug discovery and to hasten the creation of new medications for the treatment of a variety of diseases through an analysis of recent research.
Keywords: Bioinformatics, Drug discovery, Ligand-based drug design, Structure-based drug design,
Virtual screening, QSAR, Machine learning, Data assortment
INTRODUCTION
Drug Discovery by Bioinformatics Drug discovery is a complex and time-consuming process that involves the identification and development of new therapeutic compounds. Bioinformatics, a multidisciplinary field that combines biology, computer science, and statistics, plays a crucial role in accelerating and streamlining the drug discovery process. One of the key contributions of bioinformatics in drug discovery is target identification. By analyzing genomic and proteomic data, researchers can identify potential drug targets, such as specific proteins or genes associated with a particular disease.

Keyworde: Bioinformatics, Drug discovery, Ligand-based drug design, Structure-based drug design, Virtual screening, QSAR, Machine learning, Data assortment

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