Transcriptomic Data Mining and Repurposing for Computational Drug Discovery

Methods Mol Biol. 2019:1903:73-95. doi: 10.1007/978-1-4939-8955-3_5.


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.

Keywords: Computational drug discovery; Connectivity Map; Drug discovery; Drug repositioning; Drug repurposing; L1000; LINCS.

MeSH terms

  • Computational Biology / methods*
  • Data Mining* / methods
  • Databases, Factual
  • Drug Discovery* / methods
  • Drug Repositioning* / methods
  • Gene Expression Profiling / methods
  • Humans
  • Software
  • Transcriptome*
  • User-Computer Interface