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# The Chemical Checker repository
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The Chemical Checker (CC) is a data-driven resource of small molecule bioactivity data. The main goal of the CC is to express data in a format that can be used off-the-shelf in daily computational drug discovery tasks. The resource is organized in **5 levels** of increasing complexity, ranging from the chemical properties of the compounds to their clinical outcomes. In between, we consider targets, off-targets, perturbed biological networks and several cell-based assays, including gene expression, growth inhibition, and morphological profiles. The CC is different to other integrative compounds database in almost every aspect. The classical, relational representation of the data is surpassed here by a less explicit, more machine-learning-friendly abstraction of the data (see the CC [Manifesto](manifesto)).
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The Chemical Checker (CC) is a data-driven resource of small molecule bioactivity data. The main goal of the CC is to express data in a format that can be used off-the-shelf in daily computational drug discovery tasks. The resource is organized in **5 levels** of increasing complexity, ranging from the chemical properties of the compounds to their clinical outcomes. In between, we consider targets, off-targets, perturbed biological networks and several cell-based assays, including gene expression, growth inhibition, and morphological profiles.
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The CC resource is ever-growing and maintained by the [Structural Bioinformatics & Network Biology Laboratory](http://sbnb.irbbarcelona.org) at the Institute for Research in Biomedicine ([IRB Barcelona](http://irbbarcelona.org)). Should you have any questions, please send an email to [miquel.duran@irbbarcelona.org](miquel.duran@irbbarcelona.org) or [patrick.aloy@irbbarcelona.org](patrick.aloy@irbbarcelona.org).
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This project was first presented to the scientific community in the following paper: [Duran-Frigola et al., *Extending the small molecule similarity principle to all levels of biology* (2019)](https://www.dropbox.com/s/x2rqszfdfpqdqdy/duranfrigola_etal_ms_current.pdf?dl=0), and has since produced a number of [related publications](publications).
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This project was first presented to the scientific community in the following paper: [Duran-Frigola et al., *Extending the small molecule similarity principle to all levels of biology* (2019)](https://biorxiv.org/content/10.1101/745703v1), and has some [related publications](publications).
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## Source data and datasets
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... | ... | @@ -90,8 +90,4 @@ In the CC we generalize this notion to other types of data and provide functiona |
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Finding the right connectivity strategy requires a deep understanding of the datasets and, with the CC, we simplify this by pre-assigning connectivity functions to each dataset. Please note that some datasets cannot be connected to biology (e.g. 2D chemical fingerprints), whereas some others can be connected by different means (e.g. reversion/mimicking, global/local, etc.).
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For a thorough explanation of the connectivity strategies, please visit:
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* [Connectivity](connectivity)
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## Large-scale bioactivity prediction
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CC signatures can be used to robustly predict bioactivities based on previous bioactivity data. This is achieved with the module [TargetMate](bioactivity-prediction). |
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* [Connectivity](connectivity) |
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