Package: chemicalchecker
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 in5 levelsof 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.The CC resource is ever-growing and maintained by theStructural Bioinformatics & Network Biology Laboratoryat the Institute for
Research in Biomedicine (IRB Barcelona). Should you have any
questions, please send an email tomiquel.duran@irbbarcelona.orgorpatrick.aloy@irbbarcelona.org.This project was first presented to the scientific community in the
following paper:Duran-Frigola M, et al
“Extending the small-molecule similarity principle to all levels of biology with the Chemical Checker.”
Nature Biotechnology (2020) [link]and has since produced a number ofrelated publications.NoteFor an overview of the CC universe please visitbioactivitysignatures.orgSource data and datasetsThe CC is built from public bioactivity data. We are committed to
updating the resourceevery 6 months(versions named accordingly,
e.g.chemical_checker_2019_01). New datasets may be incorporated
upon request.The basic data unit of the CC is thedataset. There are 5 datalevels(AChemistry,BTargets,CNetworks,DCells
andEClinics) and, in turn, each level is divided into 5 sublevels
orcoordinates(A1-E5). Each dataset belongs to one and only
one of the 25 coordinates, and each coordinate can have a finite number
of datasets (e.g.A1.001), one of which is selected as beingexemplary.The CC is a chemistry-first biomedical resource and, as such, it
contains several predefined compound collections that are of interest to
drug discoverers, including approved drugs, natural products, and
commercial screening libraries.Signaturization of the dataThe main task of the CC is to convert raw data into formats that are
suitable inputs for machine-learning toolkits such asscikit-learn.Accordingly, the backbone pipeline of the CC is devoted to processing
every dataset and converting it to a series of formats that may be
readily useful for machine learning. The main assets of the CC are the
so-calledCC signatures:SignatureAbbreviationDescriptionAdvantagesDisadvantagesType 0sign0Raw dataset
data,
expressed
in a matrix
format.Explicit
data.Possibly
sparse,
het
erogeneous,
u
nprocessed.Type 1sign1PCA/LSI
projections
of the
data,
accounting
for 90% of
the data.Biological
signatures
of this
type can be
obtained by
simple
projection.
Easy to
compute and
require no
f
ine-tuning.Variables
dimensions,
they may
still be
sparse.Type 2sign2Networ
k-embedding
of the
similarity
network.Fixed
-length,
usually
acceptably
short.
Suitable
for machine
learning.
Capture
global
properties
of the
similarity
network.Information
leak due to
similarity
measures.
Hype
r-parameter
tunning.Type 3sign3Networ
k-embedding
of the
inferred
similarity
network.Fixed
dimension
and
available
foranymolecule.Possibly
very noisy,
hence
useless,
especially
for
low-data
datasets.NoteASignaturizermodule for direct molecule signaturization is also available.