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Transcriptomic Data Mining and Repurposing for Computational Drug Discovery

  • Yunguan Wang
  • Jaswanth Yella
  • Anil G. Jegga
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1903)

Abstract

Conventional drug discovery in general is costly and time-consuming with extremely low success and relatively high attrition rates. The disparity between high cost of drug discovery and vast unmet medical needs resulted in advent of an increasing number of computational approaches that can “connect” disease with a candidate therapeutic. This includes computational drug repurposing or repositioning wherein the goal is to discover a new indication for an approved drug. Computational drug discovery approaches that are commonly used are similarity-based wherein network analysis or machine learning-based methods are used. One such approach is matching gene expression signatures from disease to those from small molecules, commonly referred to as connectivity mapping. In this chapter, we will focus on how publicly available existing transcriptomic data from diseases can be reused to identify novel candidate therapeutics and drug repositioning candidates. To elucidate these, we will present two case studies: (1) using transcriptional signature similarity or positive correlation to identify novel small molecules that are similar to an approved drug and (2) identifying candidate therapeutics via reciprocal connectivity or negative correlation between transcriptional signatures from a disease and small molecule.

Key words

Computational drug discovery Drug repurposing Drug repositioning Connectivity Map Drug discovery LINCS L1000 

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Yunguan Wang
    • 1
  • Jaswanth Yella
    • 1
    • 3
  • Anil G. Jegga
    • 1
    • 2
    • 3
  1. 1.Division of Biomedical InformaticsCincinnati Children’s Hospital Medical CenterCincinnatiUSA
  2. 2.Department of PediatricsUniversity of Cincinnati College of MedicineCincinnatiUSA
  3. 3.Department of Computer ScienceUniversity of Cincinnati College of EngineeringCincinnatiUSA

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