Data-driven analysis and druggability assessment methods to accelerate the identification of novel cancer targets

Elsevier

Available online 24 November 2022

Computational and Structural Biotechnology JournalAuthor links open overlay panelAbstract

Over the past few decades, drug discovery has greatly improved the outcomes for patients, but several challenges continue to hinder the rapid development of novel drugs. Addressing unmet clinical needs requires the pursuit of drug targets that have a higher likelihood to lead to the development of successful drugs. Here we describe a bioinformatic approach for identifying novel cancer drug targets by performing statistical analysis to ascertain quantitative changes in expression levels between protein-coding genes, as well as co-expression networks to classify these genes into groups. Subsequently, we provide an overview of druggability assessment methodologies to prioritize and select the best targets to pursue.

Keywords

Differential expression

Pathway analysis

Drug discovery

Druggability assessment

© 2022 RGCC International GmbH. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.

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