Artificial intelligence, machine learning, and drug repurposing in cancer

Figures & data

Table 1. Drug–target interaction resources for target activity predictions

Resource	Website	Brief description	Data type*	Compounds	Targets	Interactions	Mut	Vis	API	Ref
BindingDB	https://www.bindingdb.org/bind/index.jsp	Comprehensive resource of quantitative bioactivity data in terms of IC50, AC50, Kd and Ki assays.	B	≥0.7 M	≥7.2 K	≥1.2 M			√	[25]
Cancer Genome Interpreter (CGI)	https://www.cancergenomeinterpreter.org/	Supports the identification of tumor alterations that drive the disease and flag those that may be therapeutically actionable.	C	310	837					[26]
ChEMBL	https://www.ebi.ac.uk/chembl/	Most compressive manually-curated bioactivity data from HTS of compound activities.	B, C	≥1.9 M	≥12 K	≥15.5 M	√		√	[27]
ChemicalChecker	https://chemicalchecker.org/	Provides processed, harmonized and integrated bioactivity data.	B	0.8 M	>20 K					[28]
DrugCentral	http://drugcentral.org/	Provides information on active chemical entities and drug mode of action	A, B	≥4.5 K	≥11 K	≥15 K			√	[29]
DrugTargetCommons (DTC)	http://drugtargetcommons.fimm.fi/	Manually curated bioactivity data along with protein classification into super-families, clinical phase and adverse effects as well as disease indications.	B, C	1.6 M	13 K	14.8 M	√		√	[30]
Drug Target Profiler (DTP)	http://drugtargetprofiler.fimm.fi/	Contains drug target bioactivity data and implements network visualizations. DTP also contains cell-based response profiles of the drugs and their clinical phase information.	A, B	0.9 M	6 K	4.4 M	√	√		[31]
DrugBank	https://www.drugbank.ca/	Combines drug information (i.e. chemical, pharmacological and pharmaceutical) with drug target information (i.e. sequence, structure, and pathway).	A	≥12 K	≥5 K	≥18.9 K			√	[32]
DGIdb	http://www.dgidb.org/	Drug–target interactions mined from > 30 trusted sources, including DrugBank, PharmGKB, Chembl, Drug Target Commons, Therapeutic Target Database.	A	9501	≥41 K	≥29 K			√	[33]
GtopDB	http://www.guidetopharmacology.org/	Contains quantitative bioactivity data for approved drugs and investigational compounds.	B	≥9.7 K	≥2.9 K	≥31.2 K			√	[34]
GLIDA	http://pharminfo.pharm.kyoto-u.ac.jp/services/glida/	Contains drug–target interactions only for G-protein-coupled receptors (GPCRs), which is the largest drug class today.	A	≥24 K	≥3.7 K	≥39.1 K				[35]
PubChem	https://pubchem.ncbi.nlm.nih.gov/	Provides information on chemical structures, identifiers, chemical and physical properties, biological activities, patents, health, safety, and toxicity data.	C	≥95 M	≥58 K	≥264.8 M			√	[36]
PDSP Ki	https://pdspdb.unc.edu/pdspWeb/	Contains bioactivity data in terms of Ki especially for GPCRs, ion channels, transporters and enzymes.	B	>15 K	1146	>93 K				[37]
Probes & Drugs Portal	https://www.probes-drugs.org/home/	A public resource joining together focused libraries of bioactive compounds (e.g. probes, drugs, specific inhibitor sets)	B	≥67 K	≥8.7 K	≥0.96 M				[38]
PharmGKB	https://www.pharmgkb.org/	In addition to drug–target information, contains comprehensive data of effects of genetic variation on drug response.	A	694	≥900				√	[39]
SuperTarget	http://insilico.charite.de/supertarget/	Contains information only on active drug–target interactions and drug side effects.	A	≥0.2 M	≥6.2 K	≥0.33 M				[40]
SwissTargetPrediction	http://www.swisstargetprediction.ch/	Contains information on predicted targets of drugs based on similarity principle through reverse screening.	A	≥0.38 M	≥3 K	≥0.58 M				[80]
STITCH	http://stitch.embl.de/	Contains known and predicted interactions of chemicals and proteins, as well as pathways and drug–drug interactions.	A, B	≥0.43 M	≥9.6 M			√	√	[41]

Drug/target data resources listing the number of compounds, number of targets, and the number of drug–target interactions. Mut, the database contains drug activities also for mutant proteins; Vis, implements network visualizations for drug–target interaction networks. *Data type A, contains only active drugs for the targets; B, contains quantitative bioactivity data for drug–target binding affinity; C, contains both active and inactive drug–target pairs. Table contents adapted with permission from Oxford University Press from review paper [11].

Table 2. Cell-based pharmacogenomic resources for drug efficacy predictions

Resource	Website	Brief description	Compounds	Cell lines	Patient cells	Vis	API	Ref
Connectivity Map (CMAP)	https://clue.io/cmap	A genome-scale library that catalogs transcriptional responses to chemical and genetic perturbation. CMAP contains 1 M response profiles resulting from perturbations of multiple cell types.	≥19 K	9	3176	√		[45]
Cancer Therapeutics Response Portal (CTRP)	https://portals.broadinstitute.org/ctrp/	Multidimensional profiles to explore the associations between groups of small molecules and groups of cancer cell lines at 16 concentrations.	481	860				[46]
Cancer Cell Line Encyclopedia (CCLE)	https://portals.broadinstitute.org/ccle	Large cancer cell line collections broadly capture the genomic diversity of human cancers and provide valuable insight into anti-cancer drug responses.	24	1457		√		[47]
CellMinerCDB	https://discover.nci.nih.gov/cellminercdb/	An interactive web-application that simplifies the access and exploration of cancer cell line pharmacogenomic data across different sources.	≥20 K	1000		√		[48]
Dependency Map (DepMap)	https://depmap.org/portal/	Resource for systematic identification of biomarkers of genetic vulnerabilities and drug sensitivities in hundreds of cancer models.	4686	578		√		[49]
Genomics of Drug Sensitivity in Cancer (GDSC)	http://www.cancerrxgene.org/	Screening of >1000 genetically characterized human cancer cell lines with a wide range of anti-cancer therapeutics from multiple tissue origins.	265	1001		√	√	[50]
gCSI	NA	In vitro drug testing, RNA sequencing and single-nucleotide polymorphism (SNP) array analysis of 675 human cancer cell lines.	16	675				[51]
LINCS	http://www.lincsproject.org/LINCS/	LINCS data portal contains details about the drug assays, cell types, and perturbagens that are currently part of the library, as well as software that can be used for analyzing the data	>41 K	1127				[45]
NCATS OpenData Portal	https://ncats.nih.gov/preclinical/repurpose	COVID-19-related drug repurposing data and screening a panel of SARS-CoV-2-related assays for all approved drugs	>4,895	6				[52]
Profiling Relative Inhibition Simultaneously in Mixtures (PRISM)	https://depmap.org/portal/prism/	PRISM is an experimental approach to screen thousands of drugs across hundreds of human cancer cell line models.	≥18 K	750				[53]
PharmacoDB	https://pharmacodb.pmgenomics.ca/	A web-application assembling the largest in vitro drug screens in a single database, allowing users to easily query the harmonized data from multiple studies released to date.	759	1691		√		[123]

Cell-based drug response and omics resources listing the number of compounds, number of established cell lines, and number of patient-derived primary cell samples. Vis, resource implements visualizations for compounds. Table contents adapted with permission from Oxford University Press from review paper [11].

Table 3. Pathway resources for understanding compounds’ mode of action

Resource	Website	Brief description	Proteins	Species	Compounds	Pathways	Interactions	API	Ref
PathwayCommon	http://www.pathwaycommons.org/	Pathways including biochemical reactions, complex assembly and physical interactions involving proteins, DNA, RNA, small molecules and complexes.	18,490	≥414	11,437	4,794	2.3 M	√	[55]
Kyoto Encyclopedia of Genes and Genomes (KEGG)	http://www.genome.jp/kegg/	A reference knowledge base that integrates genomic, chemical and systematic functional information.	33 M	6,221	18,749	≥541	627,677	√	[56]
Reactome	http://www.reactome.org	Manually curated and peer-reviewed pathway database with bioinformatics tools for the visualization, interpretation and analysis of pathway knowledge.	>10 K	16	1,854	2,477	>13 K	√	[57]
MetaCyc	http://metacyc.org	Curated database of experimentally elucidated metabolic pathways from all domains of life.	13,613	3,161	16,631	2,847	>16,810		[58]
SIGNOR 2.0	https://signor.uniroma2.it/	SIGnaling Network Open Resource 2.0 (SIGNOR 2.0) is a public repository that stores signaling information as binary causal relationships between biological entities.	5,229		995	49	>25 K	√	[59]
PathBank	https://pathbank.org/	PathBank is designed specifically to support pathway elucidation and pathway discovery in transcriptomics, proteomics, metabolomics and systems biology.	8,993	10	78,488	1,10,234	176,535		[60]

Table contents adapted with permission from Oxford University Press from review paper [11].

Table 4. Chemical structure databases using InchiKey searches or structure drawings

Resource	Website	Brief description	Compounds	API	Ref
ChemSpider	http://www.chemspider.com/	Provides chemical structures and physiochemical properties of small and large molecules.	≥67 M	√	[72]
ChemDB	http://cdb.ics.uci.edu/	Provides chemical structures and molecular properties. ChemDB also predicts 3D structures of molecules.	≥65 M		[73]
ChEMBL	https://www.ebi.ac.uk/chembl/	Chemical structures, bioactivity data, physiochemical properties and clinical development status of the compounds.	≥1.9 M	√	[27]
Cambridge Structural Database (CSD)	https://www.psds.ac.uk/csd	A collection small-molecule organic and organometallic crystal structures that can be visualized and downloaded.	≥1 M		[74]
ChemIDplus	https://chem.nlm.nih.gov/chemidplus/chemidlite.jsp	Contains names, synonyms and structures of the chemicals. ChemIDplus also includes links to other databases and resources.	≥0.1 M		[75]
PubChem	https://pubchem.ncbi.nlm.nih.gov/	Provides chemical structures as well quantitative bioactivity data and clinical development status of the compounds.	≥90 M	√	[36]
ZINC	http://zinc15.docking.org/	An open-access database of commercially available compounds and their structures for virtual screening.	≥230 M		[76]

Figure 1. Schematic illustration of overlaps between cancer-related gene sets. There are both target-based and non-target-based features that can be predictive of specific drug efficacies in various cancer types. The cancer genes and protein targets should be studies separately for each tissue type (e.g. breast cancer) and inhibitor class (e.g. HER2 inhibitors). Selective efficacies are preferred in the repurposing predictions, as tissue of origin-independent targets may lead to toxic side effects

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