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datasets

Datasets

Levels, coordinates and datasets

The CC is devided in five levels of increasing complexity:

Level Name Description
A Chemistry Chemical properties of the compounds.
B Targets Chemical-protein interactions.
C Networks Higher-order effects of small molecules.
D Cells Readouts of compound cell-based assays.
C Clinics Clinical data of drugs and environmental chemicals.

In turn, each level is divided in 5 sublevels or coordinates representing different aspects of the data. Each sublevel has an exemplar dataset, as described below:

Coordinate Name Description
A1 2D fingerprints Binary representation of the 2D structure of a molecule. The neighborhood of every atom is encoded using circular topology hashing.
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.
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.
A4 Structural keys 166 functional groups and substructures widely accepted by medicinal chemists (MACCS keys).
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).
B1 Mechanism of action Drug targets with known pharmacological action and modes (agonist, antagonist, etc.).
B2 Metabolic genes Drug metabolizing enzymes, transporters and carriers.
B3 Crystals Small molecules co-crystalized with protein chains. Data is organized according to the structural families of the protein chains.
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).
B5 HTS bioassays Hits from screening campaigns against protein targets (mainly confirmatory functional assays below 10 uM).
C1 Biological roles Ontology terms associated to small molecules with recognized biological roles, such as known drugs, metabolites and other natural products.
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.
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.
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.
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.
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.
D2 Cancer cell lines Small molecule sensitivity data (GI50) of a panel of 60 cancer cell lines.
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.
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.
D5 Cell bioassays Small molecule cell bioassays reported in ChEMBL, mainly growth and proliferation measurements found in the literature.
E1 Therapeutic areas Anatomical Therapeutic Chemical (ATC) codes of drugs. All ATC levels are considered.
E2 Indications Indications of approved drugs and drugs in clinical trials. A controlled medical vocabulary is used.
E3 Side effects Side effects extracted from drug package inserts via text-mining techniques.
E4 Disease phenotypes Manually curated relationships between chemicals and diseases. Chemicals include drug molecules and environmental substances, among others.
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.

Each of the coordinates can contain an arbitrary number of datasets. All datasets are fully described in the PostGreSQL database, and searchable at http://chemicalchecker.org/datasets/. They receive a numbered coding (e.g. A1.001).

Dataset structure of the