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Extending the small-molecule similarity principle to all levels of biology with the Chemical Checker

A Publisher Correction to this article was published on 21 May 2020

This article has been updated

Abstract

Small molecules are usually compared by their chemical structure, but there is no unified analytic framework for representing and comparing their biological activity. We present the Chemical Checker (CC), which provides processed, harmonized and integrated bioactivity data on ~800,000 small molecules. The CC divides data into five levels of increasing complexity, from the chemical properties of compounds to their clinical outcomes. In between, it includes targets, off-targets, networks and cell-level information, such as omics data, growth inhibition and morphology. Bioactivity data are expressed in a vector format, extending the concept of chemical similarity to similarity between bioactivity signatures. We show how CC signatures can aid drug discovery tasks, including target identification and library characterization. We also demonstrate the discovery of compounds that reverse and mimic biological signatures of disease models and genetic perturbations in cases that could not be addressed using chemical information alone. Overall, the CC signatures facilitate the conversion of bioactivity data to a format that is readily amenable to machine learning methods.

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Fig. 1: CC statistics.
Fig. 2: CC signatures visualized.
Fig. 3: Characterization of compound collections with the CC.
Fig. 4: Signature reversion of Alzheimer’s disease-specific transcriptional profiles.
Fig. 5: Discovery of chemical analogs of biologics.
Fig. 6: Representation of the CCweb resource.

Data availability

All gene expression signatures have been deposited in the GEO (GSE137202).

Code availability

To facilitate access to our data, we built a web-based resource (https://chemicalchecker.org), which includes all the bioactivity signatures in HDF5 format and the full code of the CC resource.

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Acknowledgements

We thank the SB&NB laboratory members for their support and helpful discussions. We are grateful to the Broad Institute and National Center for Advancing Translational Sciences (NCATS-NIH) for providing compounds on request, and J. Duran-Frigola for the website design. We also thank the IRB Barcelona Biostatistics and Bioinformatics Unit and the IRB Functional Genomics Facility. P.A. acknowledges the support of the Spanish Ministerio de Economía y Competitividad (grant no. BIO2016-77038-R), the INB/ELIXIR-ES (grant no. PT17/0009/0007), the European Research Council (SysPharmAD, grant no. 614944) and ‘La Caixa’ BioMedTec (grant no. CTEC_15).

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M.D.-F., E.P. and P.A. designed the study, analyzed the results and wrote the manuscript. M.D.-F. did the computational analysis, together with M.B., T.J.-B. and D.A. O.G.-P. implemented the web server. E.P. and V.A. carried out the experimental validations. All authors have read and approved the manuscript.

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Correspondence to Miquel Duran-Frigola or Patrick Aloy.

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Supplementary Information

Supplementary Data 1 and 2 legends, Supplementary Figs. 1–17 and Supplementary Tables 1–3.

Reporting Summary

Supplementary Data 1

Reversion of transcriptional signatures of fAD mutations.

Supplementary Data 2

Small-molecule analogs of biologics.

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Duran-Frigola, M., Pauls, E., Guitart-Pla, O. et al. Extending the small-molecule similarity principle to all levels of biology with the Chemical Checker. Nat Biotechnol 38, 1087–1096 (2020). https://doi.org/10.1038/s41587-020-0502-7

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