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Connectivity Map (CMAP) #

Chemical genomics is one of the application widely used to observe the genome wide molecular dynamics.

Nowadays, the artificial intelligence dominating data science, to uncover the hidden knowledge in big data era [1]. Another side, drug repurposing is effective prototype to reduce time to systematic drug screening and various trails for each and every human disease [2]. Connecting these two discipline is more important for researchers around the world to enhance the drug discovery protocol, even for the rare diseases.

In these effort, Connectivity Map (Connections among drugs, genes, and diseases) is an effective tool to match the expression profile with large public gene-expression profile datasets, which include the around 1,000,000 gene expression profiles, with highly connected Gene-Expression signature into a single web tool [3]. Particularly, chemical/drug perturbation is a more effective tool to accelerate the drug discovery process from various source. In recent time this tool was effectively used for various ethno-pharmacology [4].

The goal is to provide a generic solution to the problem by attempting to describe all biological states- physiological, disease, or induced with chemical or genetic construct- in terms of genomic signatures, creating a large public database of signature of drugs. Included categories are perturbens, Cell line, Concentration and duration of treatment, and Control perturbations. In this outcome, the researcher selected significantly reproducible 1,000 genes for further analyses.

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References #

  1. Deng, J.; Yang, Z.; Ojima, I.; Samaras, D.; Wang, F. Artificial intelligence in drug discovery: applications and techniques. Briefings in bioinformatics 2021, 10.1093/bib/bbab430, doi:10.1093/bib/bbab430.
  2. Pushpakom, S.; Iorio, F.; Eyers, P.A.; Escott, K.J.; Hopper, S.; Wells, A.; Doig, A.; Guilliams, T.; Latimer, J.; McNamee, C., et al. Drug repurposing: progress, challenges and recommendations. Nature Reviews Drug Discovery 2019, 18, 41-58, doi:10.1038/nrd.2018.168.
  3. Subramanian, A.; Narayan, R.; Corsello, S.M.; Peck, D.D.; Natoli, T.E.; Lu, X.; Gould, J.; Davis, J.F.; Tubelli, A.A.; Asiedu, J.K., et al. A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles. Cell 2017, 171, 1437-1452.e1417, doi:10.1016/j.cell.2017.10.049.
  4. Lee, J.Y.; Gallo, R.A.; Ledon, P.J.; Tao, W.; Tse, D.T.; Pelaez, D.; Wester, S.T. Integrating Differential Gene Expression Analysis with Perturbagen-Response Signatures May Identify Novel Therapies for Thyroid-Associated Orbitopathy. Translational vision science & technology 2020, 9, 39, doi:10.1167/tvst.9.9.39.