High Throughput Database

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Overview

Computer-aided drug design (CADD) plays a crucial role in accelerating drug discovery and development. Computer-aided drug design (CADD) plays a crucial role in accelerating drug discovery and development, and involves the use of computational methods to identify and design small molecules that can interact with biological targets such as proteins to modulate their activity. High-throughput databases are an important resource for CADD researchers, providing a wealth of data on genes, proteins, pathways, and diseases. In this resource page, we will explore several high-throughput databases commonly used in CADD, including David, WebGestalt, the TCGA database, systemsDOCK, and the GEO database.

High Throughput Database List

High Throughput Database	URL	Descriptions
David	https://david.ncifcrf.gov/	David, which stands for Database for Annotation, Visualization, and Integrated Discovery, is an online bioinformatics resource widely used in functional annotation and analysis of gene lists derived from high-throughput experiments. It offers a comprehensive collection of gene and protein databases, including GO terms, pathways, protein domains, and functional data. David allows users to interpret experimental datasets by identifying enriched biological themes and generating functional annotation charts.
WebGestalt	http://www.webgestalt.org/	WebGestalt is another powerful web-based tool for functional enrichment analysis. It integrates information from multiple databases and provides a user-friendly interface for analyzing gene lists and identifying enriched pathways, gene ontology categories, biological processes, and protein-protein interactions. WebGestalt also offers tools for network analysis, gene set enrichment analysis, and visualizing results.
TCGA Database	http://www.tcga.org/	The Cancer Genome Atlas (TCGA) database is a valuable resource for cancer research, providing a comprehensive collection of genomic, transcriptomic, and clinical data from a broad range of cancer types. It offers researchers access to large-scale sequencing data, including somatic mutations, gene expression profiles, DNA methylation patterns, and copy number variations. TCGA database facilitates the identification of potential drug targets and the exploration of personalized medicine approaches.
systemsDOCK	http://systemsdock.unit.oist.jp/iddp/home/index	systemsDOCK is a web server dedicated to the virtual screening of potential protein-protein interaction (PPI) inhibitors. It employs a computational approach to predict small molecules that can disrupt protein interactions involved in disease mechanisms. systemsDOCK integrates multiple databases to facilitate the identification of PPI targets and provides visualization tools to analyze protein-protein interaction networks.
GEO Database	https://www.ncbi.nlm.nih.gov/gds/	The Gene Expression Omnibus (GEO) database, maintained by the National Center for Biotechnology Information (NCBI), is a widely used resource for gene expression data. It stores high-throughput microarray and sequence-based gene expression datasets, allowing researchers to access and analyze gene expression patterns across various biological conditions and disease states. GEO database is a valuable resource for identifying potential biomarkers or drug targets.

Benefits of High Throughput Database

Novelty and Diversity: High throughput databases offer a vast collection of diverse compounds that can be screened against a target of interest. This diversity enables the identification of novel chemical entities with unique structures and properties, increasing the chances of discovering new drugs or drug leads.
Accelerated Lead Discovery: The availability of large databases allows for rapid screening of millions of compounds against a target, significantly accelerating the lead discovery process. This fast screening process enables researchers to identify potential hits or leads for further optimization and development.

Conclusion

High-throughput databases are integral to computer-aided drug design, offering vast amounts of data that can be utilized to identify potential drug targets, explore functional annotation, and analyze gene expression patterns. In this resource page, we have explored several prominent high-throughput databases used in CADD, including David, WebGestalt, TCGA Database, systemsDOCK, and GEO Database. These databases provide a wealth of information that helps to accelerate the drug discovery process and improve our understanding of complex biological systems.

For research use only. Not intended for any clinical use.

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