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# Datasets
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# Datasets
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The main xxx |
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## Level, coordinates and datasets
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The CC is devided in five levels of increasing complexity:
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|Level|Name|Description|
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|`A`|Chemistry|Chemical properties of the compounds.|
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|`B`|Targets|Chemical-protein interactions.|
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|`C`|Networks|Higher-order effects of small molecules.|
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|`D`|Cells|Readouts of compound cell-based assays.|
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|`C`|Clinics|Clinical data of drugs and environmental chemicals.|
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In turn, each level is divided in 5 sublevels representing different aspects of the data. Each sublevel has an *exemplar* dataset, as described below:
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|Coordinate|Name|Description|
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|`A1`|2D fingerprints|Binary representation of the 2D structure of a molecule. The neighborhood of every atom is encoded using circular topology hashing.|
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|`A2`|3D fingerprints|Similar to `A1`, the 3D structures of the three best conformers after energy minimization are hashed into a binary representation without the need for structural alignment.|
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|`A3`|Scaffolds|Largest molecular scaffold (usually a ring system) remaining after applying Murcko’s pruning rules. Additionally, we keep the corresponding framework, i.e. a version of the scaffold where all atoms are carbons and all bonds are single. The scaffold and the framework are encoded with path-based 1024-bit fingerprints, suitable for capturing substructures in similarity searches.|
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|`A4`|Structural keys|166 functional groups and substructures widely accepted by medicinal chemists (MACCS keys).|
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|`A5`|Physicochemistry|Physicochemical properties such as molecular weight, logP and refractivity. Number of hydrogen-bond donors and acceptors, rings, etc. Drug-likeness measurements e.g. number of structural alerts, Lipinski’s rule-of-5 violations or chemical beauty (QED).|
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|`B1`|Mechanism of action|Drug targets with known pharmacological action and modes (agonist, antagonist, etc.).|
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|`B2`|Metabolic genes Drug metabolizing enzymes, transporters and carriers.|
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|`B3`|Crystals|Small molecules co-crystalized with protein chains. Data is organized according to the structural families of the protein chains.|
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|`B4`|Binding|Compound--protein binding data available in major public chemogenomics databases. Data mainly comes from academic publications and patents. Only binding affinities below a class-specific threshold are kept (kinases ≤ 30 nM, GPCRs ≤ 100 nM, nuclear receptors ≤ 100 nM, ion channels ≤ 10 uM and others ≤ 1 uM).|
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|`B5`|HTS bioassays|Hits from screening campaigns against protein targets (mainly confirmatory functional assays below 10 uM).|
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|`C1`|Biological roles|Ontology terms associated to small molecules with recognized biological roles, such as known drugs, metabolites and other natural products.|
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|`C2`|Metabolic network|Curated reconstruction of human metabolism, containing metabolites and reactions. Data is represented as a network where nodes are metabolites and edges connect substrates and products of reactions.|
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|`C3`|Canonical pathways|Canonical pathways related to the known receptors of compounds (as recorded in `B4`). Pathways are assigned via a guilt-by-association approach, i.e. a molecule is related to a pathway if at least one of the molecule targets is a member of it.|
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|`C4`|Biological processes|Similar to `C3`, biological processes from the gene ontology are associated to compounds via a guilt-by-association approach from `B4` data. All parent terms are kept, from the leaves of the ontology to its root.|
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|`C5`|Interactomes|Neighborhoods of `B4` targets are collected by inspecting several large protein-protein interaction networks. A random-walk algorithm is used to obtain a robust measure of 'proximity' in the network.|
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|`D1`|Gene expression|Transcriptional response of cell lines upon exposure to small molecules. A well-documented reference dataset of gene expression profiles is used to map all compound profiles using a two-sided gene set enrichment analysis.
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|`D2`|Cancer cell lines|Small molecule sensitivity data (GI50) of a panel of 60 cancer cell lines.|
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|`D3`|Chemical genetics|Growth inhibition profiles in a panel of ~300 yeast mutants. Data are combined with yeast genetic interaction data, so that compounds can be assimilated to genetic alterations when they have similar profiles.|
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|`D4`|Morphology|Changes in U-2 OS cell morphology measured after compound treatment using a multiplexed-cytological cell painting assay. 812 morphology features are recorded via automated microscopy and image analysis.|
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|`D5`|Cell bioassays|Small molecule cell bioassays reported in ChEMBL, mainly growth and proliferation measurements found in the literature.|
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|`E1`|Therapeutic areas|Anatomical Therapeutic Chemical (ATC) codes of drugs. All ATC levels are considered.|
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|`E2`|Indications|Indications of approved drugs and drugs in clinical trials. A controlled medical vocabulary is used.|
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|`E3`|Side effects|Side effects extracted from drug package inserts via text-mining techniques.|
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|`E4`|Disease phenotypes|Manually curated relationships between chemicals and diseases. Chemicals include drug molecules and environmental substances, among others.|
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|`E5`|Drug-drug interactions|Changes in the effect of a drug when it is taken together with a second drug. Drug-drug interactions may alter pharmacokinetics and/or cause side effects.| |
