Unexplored therapeutic opportunities in the human genome
Open accessAbstract
In 2014, the Illuminating the Druggable Genome programme was launched to promote the exploration of currently understudied but potentially druggable proteins. This article discusses how the systematic collection and processing of a wide array of biological and chemical data as part of this programme has enabled the development of evidence-based criteria for tracking the target development level of human proteins, which indicates a substantial knowledge deficit for approximately one out of three proteins in the human proteome. It also highlights the nature of the unexplored therapeutic opportunities for major protein families. A large proportion of biomedical research and the development of therapeutics is focused on a small fraction of the human genome. In a strategic effort to map the knowledge gaps around proteins encoded by the human genome and to promote the exploration of currently understudied, but potentially druggable, proteins, the US National Institutes of Health launched the Illuminating the Druggable Genome (IDG) initiative in 2014. In this article, we discuss how the systematic collection and processing of a wide array of genomic, proteomic, chemical and disease-related resource data by the IDG Knowledge Management Center have enabled the development of evidence-based criteria for tracking the target development level (TDL) of human proteins, which indicates a substantial knowledge deficit for approximately one out of three proteins in the human proteome. We then present spotlights on the TDL categories as well as key drug target classes, including G protein-coupled receptors, protein kinases and ion channels, which illustrate the nature of the unexplored opportunities for biomedical research and therapeutic development.
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Claims the paper makes
48 of 67 cited sources checked · 30 supported · 0 flagged · accuracy not yet measured
- Premisecitation
A large share of biomedical research and therapeutic development concentrates on a small portion of the human genome.
“A large proportion of biomedical research and the development of therapeutics is focused on a small fraction of the human genome.”
- Also stated in: § Abstract · plain
“tendency to focus on a small subset of well-known genes2”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [2] Edwards, A. M. et al. Too many roads not taken. Nature 470, 163–165 (2011). source
- Also stated in: § Abstract · plain
- Findingcitation
Tclin currently consists of about 600 protein targets.
“Tclin currently consists of ~600 protein targets9”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites [9] Santos, R. et al. A comprehensive map of molecular drug targets. Nat. Rev. Drug Discov. 16, 19–34 (2017). source
The Concluding thoughts and future work section identifies Tclin as a subset of the merged drug-efficacy-target data, and Drugs, targets and therapeutic areas reports 667 unique human protein efficacy targets in the merged data. Our copy does not give a count for the Tclin subset, so it does not establish that Tclin itself contains about 600 protein targets; no page numbers are available in the supplied passages. (LLM, conf=0.82)
Experimental — accuracy not yet measured
- Interpretationcitation
Some proteins within a family lack binding sites that are compatible with drugs or can bind ligands.
“not all members of a protein family have drug-compatible or ligandable21 binding sites”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [21] Surade, S. & Blundell, T. L. Structural biology and drug discovery of difficult targets: the limits of ligandability. Chem. Biol. 19, 42–50 (2012). source
- Findingown data
The bias toward well-described proteins is confirmed across publications, patents, NIH funding, GWAS, mouse phenotype data, molecular probes, and STRING queries.
“The bias towards well-described proteins2 is confirmed not only with respect to publications but also with respect to patents, NIH funding patterns, GWAS and mouse phenotype data (data not shown), availability of molecular probes such as antibodies and small molecules, and even queries in the STRING18database”
Quote in the paperSubstantial claim2 of 2 sources checked- cites [2] Edwards, A. M. et al. Too many roads not taken. Nature 470, 163–165 (2011). source
“Too many roads not taken” (page not supplied) documents a persistent publication bias toward a small group of proteins, and “biased approach” says antibodies and small molecules are available for only a handful of well-studied proteins. The paper discusses GWAS links and a knockout phenotype, but does not establish the claimed bias across GWAS or mouse phenotype data. Our copy also does not report the claimed patterns in patents, NIH funding, or STRING queries. (LLM, conf=0.87)
Experimental — accuracy not yet measured - cites [18] Szklarczyk, D. et al. STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 43, D447–D452 (2015). source
- Findingcitation
Only 9 of the 953 genes with neuro-related IMPC phenotypes lack confirmed expression in any of the 34 neuro-related tissues tracked by IDG KMC.
“Because only 9 of the 953 genes lack confirmed expression in any of the 34 neuro-related tissues tracked by IDG KMC (for example, GTEx40,41, HPA42 and HPM43), these data suggest that the remaining 80% of this set have the potential to be associated with human neurobehavioural phenotypes, paving the way for new research avenues in this direction (see Supplementary Table S8).”
Quote in the paperSubstantial claim3 of 4 sources checked- cites [40] GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013). source
- cites [41] GTEx Consortium et al. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017). source
- cites [42] Uhlén, M. et al. Proteomics. Tissue-based map of the human proteome. Science 347, 1260419 (2015). source
- cites [43] Kim, M.-S. et al. A draft map of the human proteome. Nature 509, 575–581 (2014). source
- Findingcitation
3,644 proteins have significant confirmed OMIM disease phenotype associations.
“As many as 3,644 proteins have significant disease (confirmed OMIM11 phenotype) associations.”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [11] Amberger, J., Bocchini, C. A., Scott, A. F. & Hamosh, A. McKusick's Online Mendelian Inheritance in Man (OMIM). Nucleic Acids Res. 37, D793–D796 (2009). source
The Database Content and Organization section says OMIM includes genes associated with disease phenotypes and reports 2,239 gene entries with disease-causing mutations. It does not report 3,644 proteins with significant, confirmed phenotype associations; the reported gene count measures something different. (LLM, conf=0.84)
Experimental — accuracy not yet measured
- Interpretationcitation
Olfactory GPCRs appear to attract less interest from drug discovery programmes despite links to metabolism and ageing.
“The exception among druggable families are olfactory GPCRs, which appear to attract less interest from drug discovery programmes, despite some of these GPCRs being linked to metabolism and ageing47.”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [47] Garrison, J. L. & Knight, Z. A. Linking smell to metabolism and aging. Science 358, 718–719 (2017). source
- Findingcitation
Slightly more than half of non-olfactory GPCRs have annotated drugs and small molecules targeting them.
“Slightly more than half of the non-olfactory GPCRs have annotated drugs and small molecules targeting them53,54,61; see also TABLE 1.”
Quote in the paperNot a substantial claim2 of 3 sources checked- cites [53] Wacker, D., Stevens, R. C. & Roth, B. L. How ligands illuminate GPCR molecular pharmacology. Cell 170, 414–427 (2017). source
The Introduction section on understudied and orphan GPCRs describes GPCR-targeted drugs and annotated small-molecule interactors. It does not give a count or proportion of non-olfactory GPCRs with such annotations, so it does not support the claim that the proportion is slightly more than half. (LLM, conf=0.76)
Experimental — accuracy not yet measured - cites [54] Roth, B. L., Irwin, J. J. & Shoichet, B. K. Discovery of new GPCR ligands to illuminate new biology. Nat. Chem. Biol. 13, 1143–1151 (2017). source
The Abstract says GPCRs are frequently targeted by drugs, and the GPCR deorphanization section discusses receptors with known ligands (page numbers unavailable). Neither gives the proportion of non-olfactory GPCRs with annotated drugs or small molecules. Searches for that proportion and for known ligands found no passage establishing 'slightly more than half.' (LLM, conf=0.76)
Experimental — accuracy not yet measured - cites [61] Roth, B. L. & Kroeze, W. K. Integrated approaches for genome-wide interrogation of the druggable non-olfactory G protein-coupled receptor superfamily. J. Biol. Chem. 290, 19471–19477 (2015). source
- Findingown data
STRING 2016 access counts suggest that Tdark proteins have a lower probability of being recognized by gene name.
“Whereas ‘Counts by link’ shows a more comprehensive method to access the entire proteome, it also suggests that Tdark proteins have a lower probability of being recognized (input) by gene name.”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites [18] Szklarczyk, D. et al. STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 43, D447–D452 (2015). source
The User interface section (page unavailable) describes searching for proteins and arriving through external links, but gives no access counts or comparison involving Tdark proteins. Searches for “Tdark,” “Counts by link,” and gene-name recognition found no passage addressing the claimed lower probability. (LLM, conf=0.76)
Experimental — accuracy not yet measured
- Findingcitation
TCRD data from GWAS Catalog, OMIM and text-mined DISEASES confirmed human disease phenotypes for 191 (20%) of the 953 IMPC neuro-phenotype genes.
“Target Central Resource Database (TCRD) data from the GWAS Catalog39, OMIM11 and text-mined DISEASES13 databases confirmed human disease phenotypes for 191 (20%) of these 953 genes, ranging from neurological (for example, seizure disorders) to cognitive (for example, tauopathy) and psychotic affective disorders.”
Quote in the paperSubstantial claim3 of 3 sources checked- cites [39] MacArthur, J. et al. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res. 45, D896–D901 (2017). source
- cites [11] Amberger, J., Bocchini, C. A., Scott, A. F. & Hamosh, A. McKusick's Online Mendelian Inheritance in Man (OMIM). Nucleic Acids Res. 37, D793–D796 (2009). source
- cites [13] Pletscher-Frankild, S., Pallejà, A., Tsafou, K., Binder, J. X. & Jensen, L. J. Diseases: text mining and data integration of disease-gene associations. Methods 74, 83–89 (2015). source
- Rests on an assumption: Text-mined disease associations are valid enough to count as confirmed human disease associations.
- Premisecitation
GPCRs are important off-targets for kinase inhibitors, ion channel modulators, anti-infectives and other drug-like molecules.
“GPCRs also represent important off-targets for kinase inhibitors62,63, ion channel modulators64, anti-infectives65 and other classes of drug-like molecules53,54.”
Quote in the paperNot a substantial claim4 of 4 sources checked- cites [62] Elkins, J. M. et al. Comprehensive characterization of the Published Kinase Inhibitor Set. Nat. Biotechnol. 34, 95–103 (2016). source
- cites [63] Lin, X. et al. Life beyond kinases: structure-based discovery of sorafenib as nanomolar antagonist of 5-HT receptors. J. Med. Chem. 55, 5749–5759 (2012). source
- cites [64] Huang, X.-P. et al. Allosteric ligands for the pharmacologically dark receptors GPR68 and GPR65. Nature 527, 477–483 (2015). source
- cites [65] Chan, J. D. et al. The anthelmintic praziquantel is a human serotoninergic G-protein-coupled receptor ligand. Nat. Commun. 8, 1910 (2017). source
- Premisecitation
A handful of GPCRs, mainly biogenic amine, muscarinic and opioid receptors, are the most abundantly targeted receptors for FDA-approved medications.
“A recent analysis indicates, however, that a handful of GPCRs — mainly biogenic amine, muscarinic and opioid receptors — represent the most abundantly targeted receptors for FDA-approved medications53,54.”
Quote in the paperNot a substantial claim2 of 2 sources checked- cites [53] Wacker, D., Stevens, R. C. & Roth, B. L. How ligands illuminate GPCR molecular pharmacology. Cell 170, 414–427 (2017). source
- cites [54] Roth, B. L., Irwin, J. J. & Shoichet, B. K. Discovery of new GPCR ligands to illuminate new biology. Nat. Chem. Biol. 13, 1143–1151 (2017). source
The Abstract (page unavailable) says GPCRs are the protein family most frequently targeted by therapeutic drugs, and the paper discusses aminergic, muscarinic and opioid receptors. It does not establish that a handful of receptors in those groups are the most abundantly targeted by FDA-approved medications. (LLM, conf=0.82)
Experimental — accuracy not yet measured
- Premisecitation
One in vitro study suggested that up to one-third of 10,174 genes examined in immortalized B cells after ionizing radiation are radioresponsive.
“One in vitro study46 suggested that as many as one-third of the 10,174 genes examined in immortalized B cells following ionizing radiation are radioresponsive (GSE26835 column46 in Supplementary Table S3).”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites [46] Smirnov, D. A. et al. Genetic variation in radiation-induced cell death. Genome Res. 22, 332–339 (2012). source
The Results report 3,117 IR-responsive genes in B cells from 99 individuals after irradiation, and the Methods identify the cells as immortalized. That count is close to one-third of 10,174, but our copy does not state that 10,174 genes were examined, so the claimed denominator cannot be verified. (LLM, conf=0.86)
Experimental — accuracy not yet measured
- Premisecitation
Adgrb2-mutant mice (Tbio) showed significant antidepressant-like behaviour compared with wild-type mice.
“For example, Adgrb2-mutant mice (Tbio) showed significant antidepressant-like behaviour compared with wild-type mice70, whereas the association71 between schizophrenia and Fzd3 mutants (T) remains controversial72,73.”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites [70] Okajima, D., Kudo, G. & Yokota, H. Antidepressant-like behavior in brain-specific angiogenesis inhibitor 2-deficient mice. J. Physiol. Sci. 61, 47–54 (2011). source
- Findingcitation
217 drugs in DrugCentral act through 125 Tclin ion channels for MoA.
“There are 217 drugs annotated in DrugCentral10 as acting through 125 (T) ion channels for the MoA9.”
Quote in the paperNot a substantial claim2 of 2 sources checked- cites [10] Ursu, O. et al. DrugCentral: online drug compendium. Nucleic Acids Res. 45, D932–D939 (2017). source
The Introduction and the ‘Drug mechanism of action and pharmacological action’ section establish that DrugCentral contains drug mechanism-of-action target annotations. Table 1 reports overall target counts, but the paper does not state that 217 drugs act through 125 ion channels. The citation after ‘MoA’ in the citing sentence appears to refer to a different source. (LLM, conf=0.83)
Experimental — accuracy not yet measured - cites [9] Santos, R. et al. A comprehensive map of molecular drug targets. Nat. Rev. Drug Discov. 16, 19–34 (2017). source
In “Drugs, targets and therapeutic areas” (page unavailable), the paper says it combined DrugCentral with other databases and identifies ion channels as efficacy targets for approved drugs. It reports percentages for ion-channel targets and drugs, but does not give the claimed 217 drugs, 125 Tclin ion channels, or a DrugCentral-only count. (LLM, conf=0.86)
Experimental — accuracy not yet measured
- Rests on an assumption: DrugCentral annotations accurately reflect drug-target interactions.
- Premisecitation
Defects in ion channels underlie many major human disorders, including neuronal disorders, diabetes and heart failure.
“Defects in ion channels underlie many major disorders in humans, also known as channelopathies, including neuronal disorders76, diabetes77 and heart failure78.”
Quote in the paperSubstantial claim1 of 3 sources checked- cites [76] Kullmann, D. M. The neuronal channelopathies. Brain 125, 1177–1195 (2002). source
- cites [77] Gloyn, A. L. et al. Large-scale association studies of variants in genes encoding the pancreatic beta-cell KATP channel subunits Kir6.2 (KCNJ11) and SUR1 (ABCC8) confirm that the KCNJ11 E23K variant is associated with type 2 diabetes. Diabetes 52, 568–572 (2003). source
- cites [78] Marbán, E. Cardiac channelopathies. Nature 415, 213–218 (2002). source
- Premisecitation
Among kinases transcriptionally altered in TNBC tumours were understudied kinases MRCK-γ, PRKACB, STK32B and LTK.
“Among the kinases transcriptionally altered in the TNBC tumours were several understudied kinases, including MRCK-γ (also known as CDC42BPG), PRKACB, STK32B and leukocyte tyrosine kinase receptor (LTK).”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [75] Zawistowski, J. S. et al. Enhancer remodeling during adaptive bypass to MEK inhibition is attenuated by pharmacologic targeting of the P-TEFb complex. Cancer Discov. 7, 302–321 (2017). source
The Results section on distinct basal-like and claudin-low kinome signatures identifies CDC42BPG (MRCK-γ) as upregulated in both patient TNBC tumours and cell lines after MEK inhibition. The paper also reports broader transcriptional changes in tumour kinases, but our copy does not identify PRKACB, STK32B or LTK as altered, or describe the listed kinases as understudied (pages not provided). (LLM, conf=0.85)
Experimental — accuracy not yet measured
- Interpretationcitation
In TNBC tumours, members of the understudied Tbio and Tdark kinome are co-regulated transcriptionally with Tclin and Tchem kinases in a dynamic adaptive response to targeted inhibition.
“These findings demonstrate that in TNBC tumours in patients, members of the understudied Tbio and Tdark kinome are co-regulated transcriptionally with kinases from the Tclin and Tchem category, in a dynamic adaptive response to targeted inhibition.”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [75] Zawistowski, J. S. et al. Enhancer remodeling during adaptive bypass to MEK inhibition is attenuated by pharmacologic targeting of the P-TEFb complex. Cancer Discov. 7, 302–321 (2017). source
The Results section, “MEK inhibition induces a transcriptomic/kinome response in patient TNBC tumors” (page unavailable), reports transcriptional changes in multiple kinases after seven days of trametinib treatment. It does not classify those kinases as Tbio, Tdark, Tclin, or Tchem or establish co-regulation between those categories, so that specific part of the claim is unsupported. (LLM, conf=0.84)
Experimental — accuracy not yet measured
- Premisecitation
A recent clinical study assessed trametinib response in TNBC patients.
“The potential therapeutic importance of Tbio and Tdark kinases in the kinome is highlighted by a recent clinical study that assessed the response to trametinib, a MEK1 and MEK2 inhibitor, in TNBC patients75.”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [75] Zawistowski, J. S. et al. Enhancer remodeling during adaptive bypass to MEK inhibition is attenuated by pharmacologic targeting of the P-TEFb complex. Cancer Discov. 7, 302–321 (2017). source
- Findingown data
Across all data sources, Tdark proteins carry the smallest amount of associated data.
“Tdark proteins have the least amount of data associated with them regardless of source.”
Quote in the paperSubstantial claim - Findingown data
Tclin proteins represent only 3% of the human proteome.
“proteins in the Tclin category thus represent only a small fraction (3%) of the human proteome”
Quote in the paperNot a substantial claim - Findingown data
Data amounts increase from Tdark to Tbio to Tchem to Tclin.
“Increasing amounts of data are observed for proteins when progressing through the categories from Tdark to Tbio, Tchem and Tclin.”
Quote in the paperSubstantial claim - Findingown data
The TDL classification indicates a substantial knowledge deficit for about one in three proteins in the human proteome.
“which indicates a substantial knowledge deficit for approximately one out of three proteins in the human proteome”
- Also stated in: § The knowledge deficit. · plain
“Because of this bias, one out of three human proteins (Tdark) have been largely unstudied.”
Quote in the paperSubstantial claim - Also stated in: § The knowledge deficit. · plain
- Findingnone given
Most global revenues of the pharmaceutical industry come from drugs targeting a relatively small number of Tclin proteins.
“most of the global revenues of the pharmaceutical industry are derived from drugs that target a relatively small number of the proteins in the Tclin category”
Quote in the paperNot a substantial claim - Findingnone given
Statistical significance breaks down when comparing Tclin and Tchem.
“Statistical significance breaks down when comparing Tclin and Tchem”
Quote in the paperNot a substantial claim - Findingown data
For 175 (25%) of these target-clinical candidate pairs, therapeutic indication data from ChEMBL were available.
“For 175 (25%) of these pairs, therapeutic indication data extracted from ChEMBL highlight the different distribution among protein families”
Quote in the paperNot a substantial claim - Findingown data
144 Tchem proteins were mapped to 356 clinical phase I-III candidates, yielding 701 unique target-clinical candidate pairs.
“we mapped 144 Tchem proteins to 356 clinical (phase I-III trial) candidates, for a total of 701 unique target-clinical candidate pairs”
Quote in the paperSubstantial claim - Findingown data
Tbio assignments are frequent for transcription factors, epigenetic targets, transporters and unassigned protein families.
“Instead, Tbio assignments are quite frequent for transcription factors, epigenetic targets, transporters and unassigned protein families.”
Quote in the paperNot a substantial claim - Findingnone given
Successful promotions across TDL classes are currently rare.
“Successful ‘promotions’ across classes, such as those in TABLE 4, are currently rare.”
Quote in the paperSubstantial claim - Findingown data
Tchem proteins are overrepresented in L1000: 187 of 978 (19%) versus 7% of the proteome.
“The proportions of Tclin proteins (41 of 978; 4%) and Tchem proteins in the L1000 set (187 of 978; 19%) are also higher than expected, as these make up 3% and 7% of the proteome, respectively.”
Quote in the paperSubstantial claim- Rests on an assumption: L1000 is a set of 978 landmark genes selected for their ability to predict a large portion of total variability in large microarray experiment sets.
- Findingnone given
Among 2,788 mouse-phenotyped IMPC genes, 953 have at least one significant behavioural, neurological or other nervous system-related phenotype observation.
“Among 2,788 genes phenotyped in mice at the IMPC, 953 have at least one significant behavioural, neurological or other nervous system-related phenotype observation.”
Quote in the paperSubstantial claim - Findingown data
634 human kinases were categorized as Tclin 50, Tchem 390, Tbio 163 and Tdark 31.
“The 634 human kinases were categorized as follows: Tclin, N = 50; Tchem, N = 390; Tbio, N = 163; and Tdark, N = 31 (TABLE 1).”
Quote in the paperSubstantial claim - Findingown data
Tclin proteins are overrepresented in L1000: 41 of 978 (4%) versus 3% of the proteome.
“The proportions of Tclin proteins (41 of 978; 4%) and Tchem proteins in the L1000 set (187 of 978; 19%) are also higher than expected, as these make up 3% and 7% of the proteome, respectively.”
Quote in the paperNot a substantial claim- Rests on an assumption: L1000 is a set of 978 landmark genes selected for their ability to predict a large portion of total variability in large microarray experiment sets.
- Findingown data
Anecdotal evidence suggests that proteins can migrate from Tdark to Tclin within 12-20 years.
“Anecdotal evidence (summarized in TABLE 4) suggests that it is possible for proteins to migrate from Tdark to Tclin within 12-20 years.”
Quote in the paperSubstantial claim - Findingown data
Tdark proteins are substantially underrepresented in the L1000 set: 79 of 978 (8%) versus 35% of the proteome.
“The proportion of Tdark proteins in the L1000 set (79 of 978; 8%) is substantially smaller than would be anticipated based simply on the a priori distribution of Tdark proteins (which make up 35% of the proteome), whereas Tbio targets (671 of 978; 69%) are more common than expected, as these make up 53% of the proteome.”
Quote in the paperSubstantial claim- Rests on an assumption: L1000 is a set of 978 landmark genes selected for their ability to predict a large portion of total variability in large microarray experiment sets.
- Findingown data
Tbio proteins are overrepresented in L1000: 671 of 978 (69%) versus 53% of the proteome.
“The proportion of Tdark proteins in the L1000 set (79 of 978; 8%) is substantially smaller than would be anticipated based simply on the a priori distribution of Tdark proteins (which make up 35% of the proteome), whereas Tbio targets (671 of 978; 69%) are more common than expected, as these make up 53% of the proteome.”
Quote in the paperSubstantial claim- Rests on an assumption: L1000 is a set of 978 landmark genes selected for their ability to predict a large portion of total variability in large microarray experiment sets.
- Findingown data
Data on six highlighted targets show that proteins with little information two decades ago became attractive for drug discovery after key papers on deorphanization and protein-disease association.
“Data on the six protein targets highlighted indicate that proteins for which little information was available two decades ago (effectively Tdark) became attractive from a drug discovery perspective following key papers, namely, deorphanization and protein- disease association studies.”
Quote in the paperSubstantial claim - Findingown data
Ignoring olfactory GPCRs, these strains represent a little more than one-third of druggable genes in these protein families.
“When ignoring olfactory GPCRs, these represent a little more than one-third of the druggable genes in these protein families.”
Quote in the paperSubstantial claim - Findingown data
Of these 2,788 proteins, 827 (436 Tdark) are not associated with any NIH-funded grants between 2000 and 2015.
“Of these 2,788 proteins, 827 (436 Tdark) are not associated with any NIHfunded grants between 2000 and 2015 (Supplementary Table S8).”
Quote in the paperSubstantial claim - Findingnone given
31 ion channels are categorized as Tdark.
“A relatively small number of ion channels (31) are categorized as Tdark (TABLE 1).”
Quote in the paperNot a substantial claim - Findingown data
These understudied kinases are frequently altered in breast cancer.
“These understudied kinases are frequently altered in breast cancer.”
Quote in the paperSubstantial claim - Findingown data
Only 120 of the 1,961 proteins with significant IMPC phenotype calls and NIH funding are Tdark.
“By contrast, only 120 of the 1,961 proteins with significant IMPC phenotype calls and that are associated with NIH funding are Tdark.”
Quote in the paperSubstantial claim - Findingown data
497 drugs are known to interact with ion channels outside the MoA-related constraint.
“The number of drugs increases to 497 when querying how many drugs are known to interact with ion channels outside of the MoA-related constraint.”
Quote in the paperNot a substantial claim- Rests on an assumption: DrugCentral annotations accurately reflect drug-target interactions.
- Findingown data
Many Tchem, Tbio or Tdark kinases are altered in expression or mutated in TCGA.
“Many Tchem, Tbio or Tdrak kinases are altered in expres-sion or mutated in TCGA.”
Quote in the paperSubstantial claim - Interpretationreasoning
Some proteins cannot change disease progression when targeted therapeutically.
“not all proteins can (or will) alter the course of disease via therapeutic intervention”
Quote in the paperNot a substantial claim - Interpretationnone given
The IDG Knowledge Management Center's systematic collection and processing of genomic, proteomic, chemical and disease-related data enabled evidence-based criteria for tracking target development level of human proteins.
“systematic collection and processing of a wide array of genomic, proteomic, chemical and disease-related resource data by the IDG Knowledge Management Center have enabled the development of evidence-based criteria for tracking the target development level (TDL) of human proteins”
Quote in the paperSubstantial claim - Interpretationown data
Tdark proteins are less likely to be the subject of scientific curiosity, reflecting funding patterns and lack of information and molecular probes.
“These data show a pattern similar to that observed in FIG. 2a: Tdark proteins are less likely to be the subject of scientific curiosity, which is a reflection of funding patterns and an overall lack of information and molecular probes.”
Quote in the paperSubstantial claim - Interpretationreasoning
Concerted efforts focused on an entire target class have sometimes led to new drugs.
“Concerted efforts focused on an entire target class have sometimes led to new drugs.”
Quote in the paperNot a substantial claim - Interpretationreasoning
The absence of high-quality, well-characterized molecular probes is a root cause of the Tdark situation.
“Although our data do not allow us to establish a causal relationship, we suggest that the absence of high-quality, well-characterized molecular probes is a root cause for this situation.”
Quote in the paperSubstantial claim - Premisecitation
GSK had a comprehensive programme aimed at finding new ligands and characterizing the biology of nuclear receptors.
“For example, GlaxoSmithKline (GSK) had a comprehensive programme aimed at finding new ligands and characterizing the biology of nuclear receptors48.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [48] Kliewer, S. A., Lehmann, J. M. & Willson, T. M. Orphan nuclear receptors: shifting endocrinology into reverse. Science 284, 757–760 (1999). source
- Interpretationown data
The L1000 TDL distribution supports the existence of a knowledge deficit.
“The L1000 TDL distribution data support the existence of a knowledge deficit.”
Quote in the paperSubstantial claim - Interpretationreasoning
The IMPC strains offer clues about which biological systems could be influenced by drugs acting on poorly characterized genes.
“These IMPC strains provide evidence for biological systems that may be affected when a drug targets a gene with little-known function.”
Quote in the paperSubstantial claim- Rests on an assumption: Mouse phenotype data mapped to human orthologues are informative about human disease and drug target effects.
- Premisecitation
A bile acid receptor (FXR) agonist, obeticholic acid, reached the market after the GSK programme started.
“A bile acid receptor (FXR) agonist has reached the market since this programme started: obeticholic acid (Ocaliva), which was discovered by GSK in collaboration with the University of Perugia51 and subsequently developed by Intercept Pharmaceuticals.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [51] Pellicciari, R. et al. 6alpha-ethyl-chenodeoxycholic acid (6-ECDCA), a potent and selective FXR agonist endowed with anticholestatic activity. J. Med. Chem. 45, 3569–3572 (2002). source
- Study design and conductown data
All Tdark assignments followed the stated criteria.
“the above criteria were observed for all Tdark assignments (Supplementary Table S3).”
Quote in the paperSubstantial claim - Premisenone given
The functions of about one-third of kinases in the human kinome are poorly defined or unknown.
“However, the functions of about one-third of the kinases in this family are poorly defined or unknown.”
Quote in the paperSubstantial claim - Study design and conductown data
An independent analysis using publication and ChEMBL compound counts validates the more general TDL criteria for GPCR biological functions and chemical matter.
“However, this independent analysis validates the more general TDL criteria with respect to GPCR biological functions and corresponding chemical matter.”
Quote in the paperNot a substantial claim - Interpretationreasoning
The remaining 80% of the 953 IMPC neuro-phenotype genes have potential to be associated with human neurobehavioural phenotypes.
“Because only 9 of the 953 genes lack confirmed expression in any of the 34 neuro-related tissues tracked by IDG KMC (for example, GTEx40,41, HPA42 and HPM43), these data suggest that the remaining 80% of this set have the potential to be associated with human neurobehavioural phenotypes, paving the way for new research avenues in this direction (see Supplementary Table S8).”
Quote in the paperSubstantial claim- Rests on an assumption: Mouse neurobehavioural phenotypes can indicate potential human neurobehavioural associations.
- Interpretationreasoning
This considerably delays progress in understanding ion channel function in vivo and their role in human health.
“This considerably delays our progress in understanding ion channel function in vivo and their role in human health.”
Quote in the paperNot a substantial claim - Interpretationreasoning
Lack of specific protein sequence motifs makes it difficult to flag candidate ion channel genes for further study, even with computational assertions.
“Lack of specific protein sequence motifs makes it difficult to flag candidate genes for further study, even with computational assertions.”
Quote in the paperSubstantial claim - Interpretationreasoning
Some ion channels currently categorized as Tclin or Tchem may need further illumination regarding MoA and drug specificity.
“It is therefore conceivable that some ion channels currently categorized as Tclin or Tchem are in need of further illumination with respect to MoA and drug specificity.”
Quote in the paperNot a substantial claim - Interpretationreasoning
Given current kinase inhibitor chemistry, Tbio and Tdark kinases can likely be effectively targeted with highly selective small-molecule inhibitors.
“Given the current state of kinase inhibitor chemistry, it is very likely that Tbio and Tdark kinases can be effectively therapeutically targeted with highly selective small-molecule inhibitors.”
Quote in the paperNot a substantial claim - Interpretationreasoning
Gene redundancy increases the difficulty of revealing phenotype and precise localization, which are important for understanding physiological functions beyond ion channel activity.
“Gene redundancy increases the difficulty of revealing phenotype and precise localization, both important elements for understanding physiological functions other than ion channel activity.”
Quote in the paperSubstantial claim - Interpretationreasoning
Increased expression of many kinases in primary human tumours suggests these understudied kinases have important uncharacterized functions for tumour cell phenotype.
“The potential increased expression of many kinases in primary human tumours suggests these understudied kinases have important functions for the tumour cell phenotype that have not been characterized to date.”
Quote in the paperSubstantial claim - Interpretationreasoning
A challenge is large-scale capture of negative results, which is largely neglected in scientific literature.
“Another challenge relates to an area of knowledge largely neglected in the scientific literature: the large-scale capture of negative results.”
Quote in the paperNot a substantial claim - Interpretationnone given
Some of these ion channel drug interactions are likely responsible for side effects such as cardiac toxicity.
“Some of these interactions are likely to be responsible for side effects such as cardiac toxicity.”
Quote in the paperNot a substantial claim - Premisecitation
Due to confirmation bias, scientists tend to primarily publish successful accounts of research.
“Due to confirmation bias8, scientists have a tendency to primarily publish successful accounts of research.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [8] Nickerson, R. S. Confirmation bias: a ubiquitous phenomenon in many guises. Rev. Gen. Psychol. 2, 175–220 (1998). source
- Interpretationnone given
Difficulty in illuminating dark ion channels partly stems from replicating physiological context and expressing proteins in appropriate heteromeric pore-forming complexes.
“Part of the difficulty in illuminating dark ion channels is the replication of physiological context and expression of proteins in the appropriate heteromeric, pore-forming functional complexes.”
Quote in the paperSubstantial claim - Interpretationreasoning
These represent unexplored kinases with possible therapeutic utility.
“These represent unexplored kinases with possible therapeutic utility.”
Quote in the paperSubstantial claim
- Classified as probably not a substantial claim. 36
- May combine two claims. 39
- The quote only partly states this claim. 1
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationIMPC analysis of 1,751 unique gene knockouts found human disease genes enriched for essential genes.
“Their recent analysis of 1,751 unique gene knockouts found that human disease genes are enriched for essential genes38”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites [38] Dickinson, M. E. et al. High-throughput discovery of novel developmental phenotypes. Nature 537, 508–514 (2016). source
May combine two claims.
Findingcitation52 non-olfactory GPCRs are categorized as Tdark, and new screening platforms have begun the process of illuminating them.
“Although 52 non-olfactory GPCRs are categorized as Tdark, the availability of new screening platforms to discover chemical matter for these GPCRs has begun the process of illumination64,67,68.”
Quote in the paperSubstantial claim3 of 3 sources checked- cites [64] Huang, X.-P. et al. Allosteric ligands for the pharmacologically dark receptors GPR68 and GPR65. Nature 527, 477–483 (2015). sourceExperimental — accuracy not yet measured
- cites [67] Kroeze, W. K. et al. PRESTO-Tango as an open-source resource for interrogation of the druggable human GPCRome. Nat. Struct. Mol. Biol. 22, 362–369 (2015). source
The Results and Discussion describe PRESTO-TANGO screening of poorly annotated and orphan GPCRs and the discovery of new ligand–receptor interactions, supporting the claim that a screening platform has begun to illuminate understudied receptors. The paper does not give a count of 52 non-olfactory GPCRs or classify them as Tdark; its Abstract instead discusses more than 100 orphan GPCRs. (LLM, conf=0.86)
Experimental — accuracy not yet measured - cites [68] Lansu, K. et al. In silico design of novel probes for the atypical opioid receptor MRGPRX2. Nat. Chem. Biol. 13, 529–536 (2017). source
The Abstract describes new physical and computational screening approaches for finding ligands for understudied GPCRs, and the Results report their use to identify compounds active at MRGPRX receptors. That supports the claim that screening platforms have begun to illuminate these receptors. The paper does not give the figure of 52 non-olfactory GPCRs or categorize that group as Tdark; its Abstract instead discusses approximately 120 orphan or understudied non-olfactory GPCRs. (LLM, conf=0.86)
Experimental — accuracy not yet measured
May combine two claims.
FindingcitationNearly 59% of these GPCR disease associations stem from text mining, and 48 GPCRs have confirmed associations from at least two information channels.
“Whereas the majority of these associations (nearly 59%) stem from text mining69, 48 GPCRs have confirmed associations from at least two information channels.”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites [69] Pafilis, E. et al. The SPECIES and ORGANISMS Resources for Fast and Accurate Identification of Taxonomic Names in Text. PLoS ONE 8, e65390 (2013). source
The Introduction and the ORGANISMS web resource section describe text mining for organism names, not GPCR–disease associations. Searches for GPCRs, disease associations, 59%, and information channels found no passage supporting the cited percentage. The citation is attached to the 59% text-mining statement, not the separate count of 48 GPCRs. (LLM, conf=0.77)
Experimental — accuracy not yet measured
- Rests on an assumption: Text-mined disease associations are valid enough to count as confirmed human disease associations.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationAlterations in GPCR signalling are implicated in the pathogenesis and treatment of many disorders, including neuropsychiatric, immunological, gastrointestinal, cardiac, renal, hormonal and infectious disorders.
“Alterations in GPCR signalling are implicated in the pathogenesis and treatment of neuropsychiatric55, immunological56, gastrointestinal57,58, cardiac59, renal, hormonal, infectious60 and many other disorders53,54.”
Quote in the paperNot a substantial claim2 of 4 sources checked- cites [53] Wacker, D., Stevens, R. C. & Roth, B. L. How ligands illuminate GPCR molecular pharmacology. Cell 170, 414–427 (2017). source
- cites [54] Roth, B. L., Irwin, J. J. & Shoichet, B. K. Discovery of new GPCR ligands to illuminate new biology. Nat. Chem. Biol. 13, 1143–1151 (2017). source
The Abstract (page unavailable) describes GPCRs as frequent therapeutic targets, and the sections on maraviroc/CCR5 and biased agonists discuss HIV, psychiatric disorders and heart failure. The passages do not establish the full list of disorder categories—particularly gastrointestinal, renal and hormonal disorders—or the broad claim about their pathogenesis. (LLM, conf=0.84)
Experimental — accuracy not yet measured - cites [55] Roth, B. L., Sheffler, D. J. & Kroeze, W. K. Magic shotguns versus magic bullets: selectively non-selective drugs for mood disorders and schizophrenia. Nat. Rev. Drug Discov. 3, 353–359 (2004). source
- cites [56] Hernandez, P. A. et al. Mutations in the chemokine receptor gene CXCR4 are associated with WHIM syndrome, a combined immunodeficiency disease. Nat. Genet. 34, 70–74 (2003). source
Classified as probably not a substantial claim.
Study design and conductcitationTclin assignment relied on exhaustive manual searches of primary literature and drug labels to identify mechanism-of-action links.
“Classification into this TDL category was achieved through exhaustive manual querying of primary literature and drug labels for MoA assignments with respect to molecular (protein) targets9”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [9] Santos, R. et al. A comprehensive map of molecular drug targets. Nat. Rev. Drug Discov. 16, 19–34 (2017). source
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationGPCRs are membrane-bound cell-surface receptors that transduce signals via heterotrimeric G proteins, arrestins and other cellular transducers.
“GPCRs are membrane-bound, cell-surface receptors that transduce signals via interactions with heterotrimeric G proteins, arrestins and other cellular transducers53,54.”
Quote in the paperNot a substantial claim2 of 2 sources checkedMay combine two claims.
PremisecitationGPCRs are the largest family of druggable targets in the human genome, with 20-30% of approved drugs acting on them.
“GPCRs represent the largest family of druggable targets in the human genome9, with between 20% and 30% of approved drugs acting on them53,54.”
Quote in the paperNot a substantial claim3 of 3 sources checked- cites [9] Santos, R. et al. A comprehensive map of molecular drug targets. Nat. Rev. Drug Discov. 16, 19–34 (2017). source
In the citing sentence, this source is cited for the drug-percentage claim, not the preceding ‘largest family’ claim. The Drugs, targets and therapeutic areas section reports that rhodopsin-like GPCRs account for the therapeutic effect of 33% of approved small-molecule drugs, rather than 20–30% of approved drugs generally. Our copy provides no page number for the passage. (LLM, conf=0.88)
Experimental — accuracy not yet measured - cites [53] Wacker, D., Stevens, R. C. & Roth, B. L. How ligands illuminate GPCR molecular pharmacology. Cell 170, 414–427 (2017). source
- cites [54] Roth, B. L., Irwin, J. J. & Shoichet, B. K. Discovery of new GPCR ligands to illuminate new biology. Nat. Chem. Biol. 13, 1143–1151 (2017). source
May combine two claims.
PremisecitationIn-depth analysis found that ketamine's active metabolite HNK does not block the NMDA receptor, instead displays sustained AMPA receptor activation and lacks ketamine-related side effects.
“However, in-depth analysis of the antidepressant effects of ketamine found that its active metabolite (2R,6R)-hydroxynorketamine (HNK) does not block the NMDA receptor. Instead, HNK displays sustained activation of α-amino-3hydroxy-5-methyl-4-isoxazole propionic acid (AMPA) receptors and lacks ketamine-related side effects81.”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [81] Zanos, P. et al. NMDAR inhibition-independent antidepressant actions of ketamine metabolites. Nature 533, 481–486 (2016). sourceExperimental — accuracy not yet measured
May combine two claims.
PremisecitationPretreatment biopsies matched to post-treatment surgical specimens showed overall concordance of transcriptional kinase response to trametinib, with FRK highest mean increase and cytoplasmic BMX highest mean decrease.
“Pretreatment biopsies matched to post-treatment surgical specimens showed overall concordance of the transcriptional kinase response to trametinib, with FRK (Tchem) exhibiting the highest mean increase and cytoplasmic BMX (also Tchem) exhibiting the highest mean decrease among patients in response to a 7-day drug treatment.”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [75] Zawistowski, J. S. et al. Enhancer remodeling during adaptive bypass to MEK inhibition is attenuated by pharmacologic targeting of the P-TEFb complex. Cancer Discov. 7, 302–321 (2017). source
The Results section, “MEK inhibition induces a transcriptomic/kinome response in patient TNBC tumors” (page unavailable), reports concordant transcriptional responses in four matched basal-like tumors, with FRK showing the highest mean increase and BMX the highest mean decrease after seven days of trametinib. It does not describe BMX as cytoplasmic or label FRK and BMX “Tchem”; it also limits the concordance finding to patients 1, 2, 3, and 5, rather than all patients. (LLM, conf=0.86)
Experimental — accuracy not yet measured
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationKetamine has been postulated to act as a noncompetitive NMDA antagonist and has been used off-label as an antidepressant.
“For example, the anaesthetic ketamine, which has been postulated to act as a noncompetitive N-methyl-d-aspartate (NMDA) antagonist79, has been used off-label as an antidepressant80.”
Quote in the paperNot a substantial claim1 of 2 sources checkedClassified as probably not a substantial claim.
InterpretationreasoningThe paper suspects that artificial divisions are another reason funding to study Tdark proteins is scarce.
“We suspect this to be another reason why funding to study Tdark proteins is scarce: for functionally enigmatic proteins, or the ‘ignorome’ (REF. 85), anticipating which organ, disease or phenotype is relevant may be far from trivial.”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [85] Pandey, A. K., Lu, L., Wang, X., Homayouni, R. & Williams, R. W. Functionally enigmatic genes: a case study of the brain ignorome. PLoS ONE 9, e88889 (2014). source
Classified as probably not a substantial claim.
Findingnone givenDespite attempts to overcome this problem, the paper is not aware of an unbiased, easy mechanism to capture negative results.
“Although there are attempts to overcome this problem86, we are not aware of the existence of an unbiased, easy mechanism to capture negative results.”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [86] Pfeffer, C. & Olsen, B. R. Editorial: Journal of negative results in biomedicine. J. Negat. Results Biomed. 1, 2 (2002). source
May combine two claims.
Findingown dataTdark proteins tend not to be studied by many funded NIH R01 grants and are significantly less discussed in patents than proteins in other TDLs.
“Tdark proteins tend not to be the object of study for many funded NIH R01 grants and are significantly less discussed in patents compared with proteins in other TDLs.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataMost kinase-targeting clinical candidates are aimed at oncology, whereas GPCR- and ion channel-targeting clinical candidates are aimed at central nervous system disorders.
“most of the kinase-targeting clinical candidates are aimed at oncology applications, whereas GPCRs and ion channel-targeting clinical candidates are aimed at central nervous system disorders”
Quote in the paperNot a substantial claimThe quote only partly states this claim.
FindingreasoningMany proteins that bind approved drugs with high affinity remain in Tchem because Tclin requires MoA evidence.
“many proteins known to interact with approved drugs, even with high affinity, remain in the Tchem category”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataAmong Tclin targets, 259 (79.7%) are single proteins, 39 (12%) are complex multiprotein targets, and 25 (8.3%) are multiple proteins for non-selective drugs.
“The majority (259 or 79.7%) of these targets are single proteins, whereas 39 (12%) are complex multiprotein targets. Only 25 targets (8.3%) are comprised of multiple proteins for non-selective drugs”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataMost targeted Tchem proteins are kinases (93 unique enzymes), followed by GPCRs (31 unique receptors) and ion channels (13), with seven targets from other families.
“Most targeted proteins are kinases (93 unique enzymes), followed by GPCRs (31 unique receptors) and ion channels (13), with seven targets from other families”
Quote in the paperNot a substantial claimMay combine two claims.
Findingnone given827 GPCRs, including 421 olfactory GPCRs, are tracked by IDG KMC; 96 are Tclin and 113 are Tchem, none olfactory.
“Currently 827 GPCRs — including 421 olfactory GPCRs — are tracked by IDG KMC; of these, 96 are Tclin and 113 are Tchem (none of which are olfactory).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataTbio-disease associations are rare for druggable families such as nuclear receptors, ion channels and GPCRs, which are more likely to be Tclin or Tchem.
“It appears that Tbio-disease associations are quite rare for druggable families such as nuclear receptors, ion channels and GPCRs, as these families are more likely to be in the Tclin or Tchem category.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataBased on DrugCentral, the paper found 50 approved kinase inhibitors, including 40 small-molecule protein kinase inhibitors, of which 32 are FDA-approved, one FDA-approved protein kinase activator ingenol mebutate, and FDA-approved PIK3CD inhibitor idelalisib.
“Based on DrugCentral, we found 50 approved kinase inhibitors, including 40 small-molecule protein kinase inhibitors, of which 32 are FDA-approved, one FDA-approved protein kinase activator (ingenol mebutate) and the phosphoinositide 3-kinase subunit-δ (PIK3CD) small-molecule inhibitor idelalisib, which is also FDA-approved.”
Quote in the paperNot a substantial claim- Rests on an assumption: DrugCentral annotations accurately reflect drug-target interactions.
May combine two claims.
Findingown dataOf 62 GPCRs with significant IMPC phenotype calls, 24 are Tbio and 7 Tdark; 15 are associated with neurological and behavioural phenotypes.
“Of 62 GPCRs for which significant phenotype calls have been reported by IMPC (Supplementary Table S8), 24 are Tbio and 7 are Tdark; of these, 15 are associated with neurological and behavioural phenotypes.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataOf 447 genes with significant fold changes in GSE26835, only 26 are Tclin and 61 Tchem, while the majority (268 Tbio, 92 Tdark) are understudied.
“Of the 447 genes with significant fold changes in the GSE26835 set, only 26 are Tclin and 61 are Tchem, whereas the majority (268 Tbio, 92 Tdark) are understudied (see also Supplementary Box S5).”
Quote in the paperSubstantial claimMay combine two claims.
Findingnone givenAlpk3, a Tdark kinase, when knocked out, raises embryonic and perinatal death rates; adults that survive show serious heart abnormalities.
“knockouts of the Tdark kinase gene Alpk3 have increased embryonic and perinatal lethality, with the surviving adults displaying severe heart defects”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataFive of the six highlighted targets are modulated by at least one approved drug, placing them in Tclin.
“Five of these six targets are modulated by at least one approved drug, which places them in the Tclin category.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe same distribution patterns across TDL categories appear in every data type and show statistically significant differences.
“Distribution trends within TDL categories are consistently reproduced across all data types in FIG. 2a and have statistically significant differences (Supplementary Table S4).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataAs of November 2017, IDG-coordinated IMPC production produced 568 new knockout strains: 166 GPCRs, 141 ion channels, 238 kinases, and 23 nuclear receptors.
“As of November 2017, 568 new knockout strains had been produced: 166 GPCRs, 141 ion channels, 238 kinases and 23 nuclear receptors”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataIDG submitted 482 Tbio genes; 184 mouse lines were generated, and 145 had observed phenotypes.
“Of 482 Tbio genes submitted by IDG (135 GPCRs, 133 ion channels, 200 kinases and 14 nuclear receptors), 184 mouse lines were produced, with 145 phenotypes observed.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingnone givenAdgrd1, a Tbio GPCR, when knocked out, causes reproductive problems including female infertility and skeletal abnormalities including lower bone mineral density.
“knockouts of the Tbio GPCR gene Adgrd1 display reproductive defects, such as female infertility, and skeleton phenotype defects, such as decreased bone mineral density”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataIncluding olfactory GPCRs, 618 proteins are Tdark or Tbio; 126 non-olfactory and 51 olfactory GPCRs have significant human disease associations via OMIM, GWAS and text mining.
“Including olfactory GPCRs, 618 proteins are classified as Tdark or Tbio; of these, 126 non-olfactory GPCRs and 51 olfactory GPCRs have significant associations with human diseases via OMIM, GWAS and text mining.”
Quote in the paperSubstantial claim- Rests on an assumption: Text-mined disease associations are valid enough to count as confirmed human disease associations.
May combine two claims.
Findingnone givenIDG submitted 119 Tdark genes to IMPC; 45 mouse lines resulted, and 41 had observed phenotypes.
“Of the 119 Tdark genes (51 GPCRs, 36 ion channels and 32 kinases) submitted by IDG to IMPC, 45 mouse lines were produced, with 41 phenotypes observed.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataFor 80% of the strains, phenotype information exists, and abnormalities appear across many biological systems.
“Phenotype data are available for 80% of these strains, with abnormalities detected in numerous biological systems, including those related to development, immune function, metabolism and behaviour.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingnone given355 ion channel pore-forming and auxiliary subunits are tracked by IDG KMC.
“Currently, 355 ion channel pore-forming and auxiliary subunits are tracked by IDG KMC (TABLE 1; Supplementary Table S3).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based). May combine two claims.
Findingnone givenUnlike GPCRs or kinases, neither ion channel pore-forming subunits nor auxiliary subunits share characteristic motifs.
“Unlike GPCRs or kinases, neither pore-forming subunits nor auxiliary subunits share characteristic motifs.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Findingown dataTABLE 5 shows ten Tbio or Tdark kinases amplified in TCGA and their RNA expression in TNBC cells.
“TABLE 5 shows ten Tbio or Tdark kinases whose amplification is observed in the TCGA database, together with their RNA expression in triple-negative breast cancer (TNBC) cells.”
Quote in the paperSubstantial claimMay combine two claims.
Findingnone givenAbout 100 ion channel modulators including auxiliary subunits are reported, but no systematic list of cell type-specific auxiliary subunits for all ion channels is available.
“About 100 ion channel modulators, including auxiliary subunits, are reported, but to our knowledge, a systematic list of cell type-specific auxiliary subunits for all ion channels is not available.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. May combine two claims.
Findingnone givenLRRC8B, LRRC8C and LRRC8D are Tdark, LRRC8A is Tbio; ORAI1 is Tchem, ORAI2 and ORAI3 are Tbio; except anoctamin 1 (Tclin), all other anoctamins are Tbio.
“Currently, LRRC8B, LRRC8C and LRRC8D subunits are classified as Tdark, with the exception being the subunit LRRC8A (Tbio); ORAI1 is annotated as Tchem, wheras the ORAI2 and ORAI3 proteins are annotated as Tbio. With the exception of anoctamin 1 (Tclin), all other anoctamins are labelled Tbio.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S4 (based).
Findingnone givenNo scalable systems are currently available to study localization of functional ion channel complexes.
“Currently, there are no scalable systems available to study the localization of functional complexes.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingnone givenUnderstudied Tbio and Tdark kinases share characteristics including poorly defined integration in signalling networks, poorly defined function and regulation, lack of antibodies, and lack of selective chemical tools.
“Some of the characteristics shared by understudied Tbio and Tdark kinases include poorly defined integration of the kinase in signalling networks, poorly defined function and regulation, lack of activation-loop phospho-antibodies or immunohistochemistry-grade antibodies, and lack of selective chemical tools for functional characterization.”
Quote in the paperSubstantial claimMay combine two claims.
Findingnone givenSeven additional FDA-approved antibodies target HER2, EGFR, VEGFR2 and PDGFRα, and an FDA-approved HER2-targeting antibody-drug conjugate trastuzumab emtansine exists.
“An additional seven FDA-approved antibodies target the receptor tyrosine kinases human epidermal growth factor receptor 2 (HER2; also known as ERBB2), epidermal growth factor receptor (EGFR), vascular endothelial growth factor receptor 2 (VEGFR2) and platelet-derived growth factor receptor-α (PDGFRα), and there is also an FDA-approved HER2-targeting antibody-drug conjugate, trastuzumab emtansine (”
Quote in the paperNot a substantial claimMay combine two claims.
Findingnone givenAs of March 2017, mouse lines for 4,165 human genes have been produced with phenotypes available, and 2,788 have statistically significant phenotype calls.
“As of March 2017, mouse lines corresponding to 4,165 human genes have been produced, with phenotypes available, 2,788 of which have resulted in statistically significant phenotype calls.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingnone givenIMPC has targeted 238 kinases with 114 knockouts having significant phenotype; 22 are of current interest for phase 2 IDG.
“Currently, the IMPC has targeted 238 kinases with 114 knockouts having a significant phenotype; of the latter, 22 are of current interest for phase 2 of the IDG programme.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based).
Findingnone givenThese questions have yet to find a computer-driven, reliable answer.
“These, and other similar questions, have yet to find a computer-driven, reliable answer.”
Quote in the paperNot a substantial claimMay combine two claims.
FindingcitationAccording to DrugCentral, sorafenib inhibits 114 kinases with only 9 MoA-related targets, while sunitinib inhibits 263 kinases with 9 MoA-related targets.
“According to data in DrugCentral, sorafenib inhibits 114 kinases, of which only 9 are associated MoArelated targets, whereas sunitinib inhibits 263 kinases, of which 9 are MoA-related targets.”
Quote in the paperSubstantial claim- Rests on an assumption: DrugCentral annotations accurately reflect drug-target interactions.
May combine two claims.
Premisenone givenThe NIH launched the IDG initiative in 2014 to map knowledge gaps around proteins and promote understudied druggable proteins.
“the US National Institutes of Health launched the Illuminating the Druggable Genome (IDG) initiative in 2014”
- Also stated in: § Illuminating the Druggable Genome. · plain
“the US National Institutes of Health (NIH) launched the Illuminating the Druggable Genome (IDG) initiative in 2014”
Quote in the paperSubstantial claim- Also stated in: § Illuminating the Druggable Genome. · plain
Classified as probably not a substantial claim.
Study design and conductown dataThe Tchem cut-offs were set so that roughly 90% of activity values for drugs with a confirmed mechanism against a target in that family would be included.
“we required the bio-activity of at least one small molecule to be above a specific cut-off chosen to include about 90% of the bioactivity values of drugs with a confirmed MoA for a target from that protein family”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenTDL assignments except Tclin were performed without human curation.
“Except for Tclin, TDL assignments were performed without human curation.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
Study design and conductown dataThe first three data types were used to assign TDL for Tbio and Tdark, while the other four provide independent validation of the criteria.
“Whereas the first three data types were used to assign the TDL category for proteins in the Tbio and Tdark categories, the other four data types — derived from separate text corpora and repositories — provide independent validation of our criteria for categorization overall.”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationreasoningLack of tools leads to lack of interest, and lack of interest reduces the probability that such tools will be developed.
“Lack of tools leads to lack of interest, and lack of interest diminishes the probability of such tools being developed.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductreasoningFor every Tchem target, selectivity was not incorporated into the assignment.
“Selectivity, though important both in vivo and in vitro, could not be factored in for all Tchem targets”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationValvular heart disease is associated with anorectic agents such as fenfluramine and antimigraine medications such as ergotamine via serotonin 5-HT2B receptor agonism.
“For example, valvular heart disease is associated with anorectic agents, such as fenfluramine, and antimigraine medications, such as ergot-amine, via serotonin 5-HT2B receptor agonism66.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [66] Roth, B. L. Drugs and valvular heart disease. N. Engl. J. Med. 356, 6–9 (2007). source
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationWu and colleagues found 38 small-molecule protein kinase inhibitors.
“Wu and colleagues74 found 38 small-molecule protein kinase inhibitors.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [74] Wu, P., Nielsen, T. E. & Clausen, M. H. Small-molecule kinase inhibitors: an analysis of FDA-approved drugs. Drug Discov. Today 21, 5–10 (2016). source
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisereasoningOff-target actions within the GPCR class can be associated with severe and life-threatening side effects.
“As with other druggable target classes, off-target actions within the GPCR class can be associated with severe and lifethreatening side effects.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationThe association between schizophrenia and Fzd3 mutants remains controversial.
“For example, Adgrb2-mutant mice (Tbio) showed significant antidepressant-like behaviour compared with wild-type mice70, whereas the association71 between schizophrenia and Fzd3 mutants (T) remains controversial72,73.”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [71] Katsu, T. et al. The human frizzled-3 (FZD3) gene on chromosome 8p21, a receptor gene for Wnt ligands, is associated with the susceptibility to schizophrenia. Neurosci. Lett. 353, 53–56 (2003). source
- cites [72] Wei, J. & Hemmings, G. P. Lack of a genetic association between the frizzled-3 gene and schizophrenia in a British population. Neurosci. Lett. 366, 336–338 (2004). source
- cites [73] Jeong, S. H., Joo, E. J., Ahn, Y. M., Lee, K. Y. & Kim, Y. S. Investigation of genetic association between human Frizzled homolog 3 gene (FZD3) and schizophrenia: results in a Korean population and evidence from meta-analysis. Psychiatry Res. 143, 1–11 (2006). source
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationSeveral FXR agonists are currently in clinical development.
“Currently, several FXR agonists are in clinical development52.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [52] Hambruch, E., Kinzel, O. & Kremoser, C. On the pharmacology of farnesoid X receptor agonists: give me an 'A', like in 'acid'. Nucl. Recep. Res. 3, 101207 (2016). source
Probably not a substantial claim: may not pass S4 (based).
Premisenone givenThe ~600-member human kinome is highly druggable using competitive and allosteric small-molecule inhibitors.
“The ~600-member human kinome (Supplementary Table S3) is made up of protein kinases, in addition to metabolic and lipid kinases, and is highly druggable using both competitive and allosteric small-molecule inhibitors.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
InterpretationreasoningThe paucity of antibodies and small molecules diminishes the ability to subject Tdark proteins to scientific inquiry.
“Indeed, the paucity of antibodies and small molecules (criteria that help define Tbio and proteins diminishes our ability to subject Tdark proteins to scientific inquiry.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S2 (checkable).
Interpretationnone givenThe TDL classification scheme provides a convenient way to partition human targets and highlights focus or lack of science and drug discovery efforts.
“The TDL classification scheme provides a convenient way to partition human targets that highlights the focus (or lack thereof) of science and drug discovery efforts on different targets.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Premisenone givenThe only other deliberate targeted effort to study Tdark proteins besides IDG is IMPC.
“As mentioned above, the only other deliberate targeted effort to study Tdark proteins in addition to IDG is the IMPC.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningThrough TDL groupings, knowledge accumulation and deficits can be highlighted for target families, with the common theme that much is known but a large fraction of the proteome remains understudied.
“Through the use of the TDL groupings, we can highlight knowledge accumulation, as well as deficits, for a variety of target families, with a common theme being that while much is known, there remains a large fraction of the proteome that is understudied.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S4 (based).
Premisenone givenIon channels mediate signalling within cells, between cells, and between cells and their environment.
“Ion channels mediate signalling within cells, between cells, and between cells and their environment.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningThese artificial divisions can prevent scientists from achieving a translational, integrative view of gene and protein function.
“These artificial divisions can prevent scientists from achieving a translational, integrative view of gene and protein function.”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationown dataIDG KMC enables quantitative demonstration of a knowledge deficit for dark and understudied proteins, underscoring the need for basic science.
“The IDG KMC enables us to quantitatively demonstrate the existence of a knowledge deficit with respect to dark and understudied proteins, which underscores the need for basic science and its major role in illuminating gene functions and roles in human disease.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningNon-positive facts fit poorly to current publishing and citation paradigms.
“However, such non-positive facts fit poorly to current publishing and citation paradigms.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationLRRC8 heteromers form volume-regulated anion channels, ORAI proteins assemble to form CRACs, and anoctamins are olfactory calcium-activated chloride channels.
“For example, leucine-rich repeat-containing protein 8 (LRRC8) heteromers form82 volume-regulated anion channels (VRACs), and ORAI proteins assemble to form83 calcium-releaseactivated calcium channels (CRACs), whereas anoctamins are olfactory calcium-activated chloride channels (CACCs)84.”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [82] Pedersen, S. F., Klausen, T. K. & Nilius, B. The identification of a volume-regulated anion channel: an amazing Odyssey. Acta Physiol. 213, 868–881 (2015). source
- cites [83] Niemeyer, B. A. Changing calcium: CRAC channel (STIM and Orai) expression, splicing, and posttranslational modifiers. Am. J. Physiol. Cell Physiol. 310, C701–709 (2016). source
- cites [84] Dauner, K., Lissmann, J., Jeridi, S., Frings, S. & Möhrlen, F. Expression patterns of anoctamin 1 and anoctamin 2 chloride channels in the mammalian nose. Cell Tissue Res. 347, 327–341 (2012). source
Classified as probably not a substantial claim.
Interpretationnone givenConfirmed disease associations or receptor deorphanization remain major incentives to allocate resources and further study Tdark proteins.
“Confirmed associations with a specific disease, or receptor deorphanization (TABLE 4), remain major incentives to allocate resources and further study of Tdark proteins.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S4 (based).
Premisenone givenMost ion channels have paralogues that function redundantly.
“Moreover, most ion channels have paralogues that function redundantly.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
Interpretationnone givenA key challenge for IDG KMC and other drug discovery data analysts is reliable predictions about next experiments, phenotypes and pathways.
“Finally, a key challenge faced by IDG KMC, and perhaps by other data analysts working in drug discovery, is that of reliable predictions: when examining Tdark proteins in particular, experimentalists would like to know what experiment to do next, what phenotypic changes should be examined first and which pathway is relevant in a specific disease.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based).
Interpretationnone givenThis distinction breaks down in nature, as the same genes and pathways likely interact regardless of organ, albeit context-specifically.
“This distinction breaks down in nature, as we are likely to observe the interplay between the same genes and pathways regardless of organ, albeit in a contextspecific manner.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.
Premisenone givenModern medicine often employs artificial distinctions by organ or disease, and these separations carry into research.
“Modern medicine often employs artificial distinctions in terms of what and how biological systems are studied: segregated by organ (for example, ophthalmology and cardiology) or by disease (for example, oncology and infectious diseases), medical specialty separations carry over into the research arena, both in academia and industry.”
Quote in the paperNot a substantial claim
The paper relies on this without support.
Mouse phenotype data mapped to human orthologues are informative about human disease and drug target effects.
“The IDG KMC incorporates gene-centric mouse phenotype data and maps these data to the respective human orthologues.”
“These IMPC strains provide evidence for biological systems that may be affected when a drug targets a gene with little-known function.”
The paper relies on this without support.
The integrated data sources are complete and accurate enough to quantify knowledge about human proteins.
“Data-driven drug discovery strategies rely on the integration of proprietary and internal data with third-party resources”
“data heterogeneity, complexity and incompleteness, as well as contextual information and metadata capture, pose substantial barriers to reliable systematic analyses of all data required to address biomedical research questions”
The paper relies on this without support.
L1000 is a set of 978 landmark genes selected for their ability to predict a large portion of total variability in large microarray experiment sets.
“L1000 is a set of 978 landmark genes that have been selected for their ability to predict a large portion of the total variability seen in large sets of microarray experiments.”
The paper relies on this without support.
Mouse neurobehavioural phenotypes can indicate potential human neurobehavioural associations.
“these data suggest that the remaining 80% of this set have the potential to be associated with human neurobehavioural phenotypes”
The paper relies on this without support.
Text-mined disease associations are valid enough to count as confirmed human disease associations.
“Target Central Resource Database (TCRD) data from the GWAS Catalog39, OMIM11 and text-mined DISEASES13 databases confirmed human disease phenotypes for 191 (20%) of these 953 genes”
The paper relies on this without support.
The TDL categories meaningfully reflect knowledge and druggability status of proteins.
“The TDL classification scheme provides a convenient way to partition human targets that highlights the focus (or lack thereof) of science and drug discovery efforts on different targets.”
The paper relies on this without support.
DrugCentral annotations accurately reflect drug-target interactions.
“According to data in DrugCentral, sorafenib inhibits 114 kinases, of which only 9 are associated MoArelated targets, whereas sunitinib inhibits 263 kinases, of which 9 are MoA-related targets.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Many poorly characterised proteins are linked to disease, including those that are druggable.
“This is despite clear evidence from studies of gene expression and genetic variation that many of the poorly characterised proteins are linked to disease, including those that are eminently druggable [6,7].”
From Functional unknomics: Systematic screening of conserved genes of unknown function (2023) · cites this paper as [6]
Biological research largely ignores less well known, but potentially important, genes.
“that biological research largely ignores less well known, but potentially important, genes [2,4,6,7].”
From Functional unknomics: Systematic screening of conserved genes of unknown function (2023) · cites this paper as [6]
Machine learning is increasingly being explored as target identification technology, often by means of knowledge graphs.
“Although novel drug targets are typically identified through genomic, proteomic or phenotypic experiments, machine learning is increasingly being explored as target identification technology (52), often by means of knowledge graphs (53).”
From Artificial Intelligence for Drug Discovery: Are We There Yet? (2024) · cites this paper as [52]
The Target development levels section discusses data-based target prioritization, and the Conclusions discuss the difficulty of making reliable computer-driven predictions. Neither passage says that machine learning is increasingly being explored for target identification (searched: “machine learning,” “target identification,” and computational prediction). The knowledge-graph clause has a separate citation, so it is not attributed to this source. (LLM, conf=0.72)
Experimental — accuracy not yet measuredOnly around 20% of the human proteome is considered druggable with small-molecule antagonists.
“Only around 20% of the human proteome is considered to be druggable with small-molecule antagonists.”
From Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence (2023) · cites this paper as [3]
Pharos and TCRD jointly serve as the knowledge hub for over 20 000 human protein targets.
“Pharos and the Target Central Resource Database (TCRD) are open-access resources developed as a part of the IDG program and jointly serve as the knowledge hub for over 20 000 human protein targets (2,3).”
From TCRD and pharos 2021: mining the human proteome for disease biology (2021) · cites this paper as [3]
Snail1 is almost undruggable.
“Being a transcription factor, Snail1 is almost undruggable19”
From Bioactivity descriptors for uncharacterized chemical compounds (2021) · cites this paper as [19]
Chemical probes are lacking for the vast majority of human proteins.
“Despite their basic and translational value, chemical probes are lacking for the vast majority of human proteins6.”
From Expedited mapping of the ligandable proteome using fully functionalized enantiomeric probe pairs (2019) · cites this paper as [6]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Submitted Tdark ion-channel genes exceed the stated Tdark total
MinorThe paper says 36 Tdark ion-channel genes were submitted to IMPC, but elsewhere states that only 31 ion channels are categorized as Tdark. It gives no different classification date that would reconcile the counts.
“Of the 119 Tdark genes (51 GPCRs, 36 ion channels and 32 kinases) submitted by IDG to IMPC”
“A relatively small number of ion channels (31) are categorized as Tdark (TABLE 1).”
Submitted Tdark kinase genes exceed the stated Tdark total
MinorThe 32 Tdark kinase genes reported as submitted to IMPC exceed the paper's stated total of 31 Tdark human kinases. The text does not identify these as counts from different classifications.
“Of the 119 Tdark genes (51 GPCRs, 36 ion channels and 32 kinases) submitted by IDG to IMPC”
“The 634 human kinases were categorized as follows: Tclin, N = 50; Tchem, N = 390; Tbio, N = 163; and Tdark, N = 31 (TABLE 1).”
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NIH Molecular Libraries Initiative
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On the thresholds of knowledge
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Self-citationPredicting protein druggability
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Allosteric ligands for the pharmacologically dark receptors GPR68 and GPR65
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Self-citationData completeness—the Achilles heel of drug-target networks
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The Genotype-Tissue Expression (GTEx) project
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Self-citationStructural Biology and Drug Discovery of Difficult Targets: The Limits of Ligandability
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Target selection in drug discovery
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“Respiratory disorders 3 (7) 1 (1) 1 (1) –”
The SPECIES and ORGANISMS Resources for Fast and Accurate Identification of Taxonomic Names in Text
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Self-citationAdvancing Biological Understanding and Therapeutics Discovery with Small-Molecule Probes
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Drug discovery effectiveness from the standpoint of therapeutic mechanisms and indications
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The identification of a volume‐regulated anion channel: an amazing<scp>O</scp>dyssey
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“Unmapped 28 (58) 11 (23) 87 (175) 5 (14)”
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Can literature analysis identify innovation drivers in drug discovery?
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Discovery of new GPCR ligands to illuminate new biology
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Self-citationFunctionally Enigmatic Genes: A Case Study of the Brain Ignorome
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Self-citationThe anthelmintic praziquantel is a human serotoninergic G-protein-coupled receptor ligand
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Self-citationDrug target ontology to classify and integrate drug discovery data
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Self-citationEditorial: Journal of Negative Results in Biomedicine
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Expression patterns of anoctamin 1 and anoctamin 2 chloride channels in the mammalian nose
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Best-selling human medicines 2002-2004
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Antidepressant-like behavior in brain-specific angiogenesis inhibitor 2-deficient mice
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III. μ-Opioid receptors in the enteric nervous system
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Genetic variation in radiation-induced cell death
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Linking smell to metabolism and aging
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Changing calcium: CRAC channel (STIM and Orai) expression, splicing, and posttranslational modifiers
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Lack of a genetic association between the frizzled-3 gene and schizophrenia in a British population
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Ketamine for depression: the highs and lows
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Self-citationOn the Pharmacology of Farnesoid X Receptor Agonists: Give me an "A", Like in "Acid"
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Flagged first, then by citations
Cited by
Sorted by citations
The reactome pathway knowledgebase 2022
Gillespie, M. E., Jassal, B., Stephan, R., Orlic-Milacic, M., Rothfels, K., Senff‐Ribeiro, A., Griss, J., Sevilla, C., Matthews, L. R., Gong, C., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab1028 · Cited by 2,355 · see citing works
“The efficacy of Secukinumab, an IL17 inhibitor (GSE137430, transcriptomics) (17), Ustekinumab, an IL23/IL12 inhibitor (GSE140684, microarray) (18), and a JAK/SYK inhibitor (GSE133385, microarray) (19) was each compared with placebo.”
The Reactome Pathway Knowledgebase 2024
Orlic-Milacic, M., Beavers, D., Conley, P., Gong, C., Gillespie, M. E., Griss, J., Haw, R., Jassal, B., Matthews, L. R., May, B., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad1025 · Cited by 1,511 · see citing works
“A recent survey ( 9 ) suggests that experimental evidence is available for ∼ 68% of these predicted gene products, or 13 500, leaving ∼ 2350 annotatable human proteins not yet in Reactome and ∼ 6300 ‘dark’ proteins ( 10 ) whose functions are not yet directly accessible for annotation.”
BackgroundZdrazil, B., Félix, E., Hunter, F. M. I., Manners, E. J., Blackshaw, J., Corbett, S., Veij, M. D., Ioannidis, H., Méndez, D., Morales, J. F. M., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad1004 · Cited by 1,463 · see citing works
“We have focused on identifying patents with bioactivity data for small molecules against understudied targets, in collaboration with the Illuminating the Druggable Genome project ( 25 ).”
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Self-citationTrends in kinase drug discovery: targets, indications and inhibitor design
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G protein-coupled receptors: structure- and function-based drug discovery
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How many rare diseases are there?
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Self-citationOpen Targets Platform: new developments and updates two years on
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Development of targeted protein degradation therapeutics
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Mapping the Degradable Kinome Provides a Resource for Expedited Degrader Development
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Therapies for rare diseases: therapeutic modalities, progress and challenges ahead
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Self-citationProperties of FDA-approved small molecule protein kinase inhibitors: A 2021 update
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A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical models
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“6a and Supplementary Information 9).”
InfluentialSchneider, M., Radoux, C. J., Hercules, A., Ochoa, D., Dunham, I., Zalmas, L.-P., Hessler, G., Ruf, S., Shanmugasundaram, V., Hann, M. M., et al. (2021) · Nature Reviews Drug Discovery · doi:10.1038/s41573-021-00245-x · Cited by 273 · see citing works
Self-citationDiscovery of Human Signaling Systems: Pairing Peptides to G Protein-Coupled Receptors
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“, 2013), and illumination of understudied drug targets is a key objective of modern drug discovery (Oprea et al., 2018; Roth and Kroeze, 2015).”
BackgroundSelf-citationArtificial Intelligence for Drug Discovery: Are We There Yet?
Hasselgren, C., & Oprea, T. I. (2024) · Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-040323-040828 · Cited by 236 · see citing works
“mode-of-action for the therapeutic modality (51).”
Self-citationPolypharmacology: The science of multi-targeting molecules
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Understudied proteins: opportunities and challenges for functional proteomics
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“( 18 ), we classified both known and predicted target proteins into four categories, namely T clin (clinic), T chem (chemistry), Tbio (biology) and Tdark (dark genome) ( 18 ).”
BackgroundInvestigating Cryptic Binding Sites by Molecular Dynamics Simulations
Kuzmanic, A., Bowman, G. R., Juárez‐Jiménez, J., Michel, J., & Gervasio, F. L. (2020) · Accounts of Chemical Research · doi:10.1021/acs.accounts.9b00613 · Cited by 195 · see citing works
DrugCentral 2021 supports drug discovery and repositioning
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“Our knowledge-based protein classification (19) bins human proteins into four categories, according to their ‘target development level’ (TDL): Tclin are MoA-designated drug targets via which approved drugs act (15,20,21), currently 659 human proteins; Tchem are proteins that are not Tclin, but are known to bind small…”
BackgroundSelf-citationLINCS Data Portal 2.0: next generation access point for perturbation-response signatures
Stathias, V., Turner, J., Koleti, A., Vidovic, D., Cooper, D., Fazel-Najafabadi, M., Pilarczyk, M., Terryn, R., Chung, C., Umeano, A., et al. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkz1023 · Cited by 161 · see citing works
“net/) (24) provide starting points for the validation of new prospective drug targets.”
BackgroundSelf-citationExpedited mapping of the ligandable proteome using fully functionalized enantiomeric probe pairs
Wang, Y., Dix, M. M., Bianco, G., Remsberg, J. R., Lee, H.-Y., Kalocsay, M., Gygi, S. P., Forli, S., Vite, G., Lawrence, R. M., et al. (2019) · Nature Chemistry · doi:10.1038/s41557-019-0351-5 · Cited by 179 · see citing works
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“In comparison with other omics levels, it is relatively poorly characterized [54].”
BackgroundPROTACs to address the challenges facing small molecule inhibitors
Martín-Acosta, P., & Xiao, X. (2020) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2020.112993 · Cited by 186 · see citing works
Target 2035: probing the human proteome
Carter, A. J., Kraemer, O., Zwick, M., Mueller‐Fahrnow, A., Arrowsmith, C. H., & Edwards, A. M. (2019) · Drug Discovery Today · doi:10.1016/j.drudis.2019.06.020 · Cited by 169 · see citing works
Analytical and Biochemical Perspectives of Protein O-GlcNAcylation
Ma, J., Wu, C., & Hart, G. W. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c00884 · Cited by 171 · see citing works
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Self-citationTCRD and pharos 2021: mining the human proteome for disease biology
Sheils, T. K., Mathias, S. L., Kelleher, K. J., Siramshetty, V. B., Nguyen, D.-T., Bologa, C. G., Jensen, L. J., Vidović, D., Koleti, A., Schürer, S. C., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa993 · Cited by 160 · see citing works
“Pharos and the Target Central Resource Database (TCRD) are open-access resources developed as a part of the IDG program and jointly serve as the knowledge hub for over 20 000 human protein targets (2,3).”
BackgroundSelf-citationWill Artificial Intelligence for Drug Discovery Impact Clinical Pharmacology?
Zhavoronkov, A., Vanhaelen, Q., & Oprea, T. I. (2020) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1795 · Cited by 159 · see citing works
“, their function and role in human biology and disease is not understood).(3) The fact that in-depth biological insights are limited to a small number of proteins(4) is (for the most part) due to a causality dilemma: The study of specific genes and proteins requires specific biomolecular tools (e.”
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Self-citationComputational Drug Design Methods—Current and Future Perspectives
Prieto-Martínez, F. D., López‐López, E., Juárez‐Mercado, K. E., & Medina‐Franco, J. L. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-816125-8.00002-x · Cited by 152 · see citing works
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“), as identified in literature, patents, drug labels, NIH funded grants and other sources (16).”
BackgroundSelf-citationTarget-Based Evaluation of “Drug-Like” Properties and Ligand Efficiencies
Leeson, P. D., Bento, A. P., Gaulton, A., Hersey, A., Manners, E. J., Radoux, C. J., & Leach, A. R. (2021) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.1c00416 · Cited by 164 · see citing works
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A review of biomedical datasets relating to drug discovery: a knowledge graph perspective
Bonner, S., Barrett, I. P., Ye, C., Swiers, R., Engkvist, O., Bender, A., Hoyt, C. T., & Hamilton, W. L. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac404 · Cited by 150 · see citing works
Cell-Free Protein Synthesis: A Promising Option for Future Drug Development
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Messner, C. B., Demichev, V., Wang, Z., Hartl, J., Kustatscher, G., Mülleder, M., & Ralser, M. (2022) · PROTEOMICS · doi:10.1002/pmic.202200013 · Cited by 134 · see citing works
Drug Design—Past, Present, Future
Doytchinova, I. A. (2022) · Molecules · doi:10.3390/molecules27051496 · Cited by 121 · see citing works
“[28] divided the whole proteome into four groups according to the target development level, i.”
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Soluble ligands as drug targets
Attwood, M. M., Jonsson, J., Rask‐Andersen, M., & Schiöth, H. B. (2020) · Nature Reviews Drug Discovery · doi:10.1038/s41573-020-0078-4 · Cited by 110 · see citing works
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Knowledge graphs and their applications in drug discovery
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“Studies have shown that therapeutic opportunities [95] and essentiality [96] of well-researched genes are similar to those in the unstudied ’dark’ genome.”
BackgroundGilbert, K. E., Vuorinen, A., Aatkar, A., Pogány, P., Pettinger, J., Grant, E. K., Kirkpatrick, J. M., Rittinger, K., House, D., Burley, G. A., et al. (2023) · ACS Chemical Biology · doi:10.1021/acschembio.2c00633 · Cited by 100 · see citing works
DrugCentral 2023 extends human clinical data and integrates veterinary drugs
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“According to the Target Development Level classification system of human proteins (31) adopted within the IDG consortium, 39 new targets were annotated as Tclin, i.”
BackgroundMethodSelf-citationComputational drug repurposing: approaches, evaluation of in silico resources and case studies
Tanoli, Z., Fernández‐Torras, A., Özcan, U. O., Kushnir, A., Nader, K. M., Gadiya, Y., Fiorenza, L., Ianevski, A., Vähä-Koskela, M. J. V., Miihkinen, M., et al. (2025) · Nature Reviews Drug Discovery · doi:10.1038/s41573-025-01164-x · Cited by 99 · see citing works
“In this context, making use of a collection of nearly 2,000 GWAS across several diseases, as well as close to 750,000 drug-induced perturbation signatures, PharmGWAS serves as a discovery platform and web tool for the discovery of new repurposable drugs, as well as potential drug combinations 42 .”
Bioactivity descriptors for uncharacterized chemical compounds
Bertoni, M., Duran-Frigola, M., Badia-i-Mompel, P., Pauls, E., Orozco-Ruiz, M., Guitart-Pla, O., Alcalde, V., Diaz, V. M., Berenguer-Llergo, A., Brun-Heath, I., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-24150-4 · Cited by 92 · see citing works
“Being a transcription factor, Snail1 is almost undruggable 19 , and we looked for indirect strategies to inhibit its function.”
Large-scale chemoproteomics expedites ligand discovery and predicts ligand behavior in cells
Offensperger, F., Tin, G., Duran‐Frigola, M., Hahn, E., Dobner, S., Ende, C. W. A., Strohbach, J. W., Rukavina, A., Brennsteiner, V., Ogilvie, K., et al. (2024) · Science · doi:10.1126/science.adk5864 · Cited by 95 · see citing works
“However,althoughtheimportanceofsmallmol-eculesisundeniable,around80%ofallhuman proteinsstilllackchemicalligands,motivating community-wideliganddiscoveryefforts( 6 , 7 ).”
AlphaFold illuminates half of the dark human proteins
Binder, J. L., Berendzen, J., Stevens, A. O., He, Y., Wang, J., Dokholyan, N. V., & Oprea, T. I. (2022) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2022.102372 · Cited by 81 · see citing works
Self-citationCritical Assessment of Targeted Protein Degradation as a Research Tool and Pharmacological Modality
Kostić, M., & Jones, L. H. (2020) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2020.02.006 · Cited by 96 · see citing works
“This would open opportunities to target POIs that are currently considered to be “undruggable” [40].”
BackgroundGeneshot: search engine for ranking genes from arbitrary text queries
Lachmann, A., Schilder, B. M., Wojciechowicz, M. L., Torre, D., Kuleshov, M. V., Keenan, A. B., & Ma’ayan, A. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz393 · Cited by 88 · see citing works
“This approach for performing research has some drawbacks, for example: (i) Research that describes the functions and interactions of genes and proteins has strong biases toward studying popular genes while ignoring most others (1,2); (ii) It is also common that researchers are overwhelmed with the growing volume of pu…”
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Therapeutics—how to treat phase separation-associated diseases
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Crowdsourced mapping of unexplored target space of kinase inhibitors
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Self-citationFunctional unknomics: Systematic screening of conserved genes of unknown function
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Comprehensive Analysis of ERK1/2 Substrates for Potential Combination Immunotherapies
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“or kinases [83], whether and how they may be combined with other therapies remains challenging (see Outstanding Questions).”
BackgroundDruggable genome and precision medicine in cancer: current challenges
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The virtuous cycle of human genetics and mouse models in drug discovery
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Recent advances in drug repurposing using machine learning
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The Omnipresence of DYRK1A in Human Diseases
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“Over the past 25 years, technological advances in genomics and analytical approaches have permitted the dissection of the human genome in unprecedented depth, greatly expanding our understanding of many diseases, and facilitating the identification of currently understudied, but potentially druggable, proteins [3,4].”
BackgroundCheminformatics Tools for Analyzing and Designing Optimized Small Molecule Collections and Libraries
Moret, N., Clark, N. A., Hafner, M. A., Wang, Y., Lounkine, E., Medvedovic, M., Wang, J., Gray, N. S., Jenkins, J. L., & Sorger, P. K. (2018) · Cell chemical biology · doi:10.1016/j.chembiol.2019.02.018 · Cited by 65 · see citing works
“…of the kinome nonetheless remains unaddressed with available chemical tools (one motivation for the NIH Illuminating the Druggable Genome program (Oprea et al., 2018)) Because our approach is data-driven and considers only public information, some differences exist between our top picks and…”
BackgroundCalibrated geometric deep learning improves kinase–drug binding predictions
Luo, Y., Liu, Y., & Peng, J. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00751-0 · Cited by 67 · see citing works
Genetics of extreme human longevity to guide drug discovery for healthy ageing
Zhang, Z. D., Milman, S., Lin, J.-R., Wierbowski, S. D., Yu, H., Barzilai, N., Gorbunova, V. A., Ladiges, W., Niedernhofer, L. J., Suh, Y., et al. (2020) · Nature Metabolism · doi:10.1038/s42255-020-0247-0 · Cited by 61 · see citing works
A PKA inhibitor motif within SMOOTHENED controls Hedgehog signal transduction
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Self-citationThe 2022Nucleic Acids Researchdatabase issue and the online molecular biology database collection
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“In the ‘Metabolic and signalling pathways’ section, the popular Reactome database of biological processes and networks has an Update paper (46) describing an interesting collaboration with the ‘Illuminating the Druggable Genome’ (IDG) consortium (47) that helps place many ‘dark’ proteins (those that are poorly underst…”
BackgroundDeep contrastive learning enables genome-wide virtual screening
Jia, Y., Gao, B., Tan, J., Zheng, J., Hong, X., Zhu, W., Tan, H., Xiao, Y., Tan, L., Cai, H., et al. (2026) · Science · doi:10.1126/science.ads9530 · Cited by 66 · see citing works
Recent progress on cheminformatics approaches to epigenetic drug discovery
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“…field, several epigenetic proteins associated with important diseases have been largely understudied, such as protein arginine methyltransferases (PRMTs), histone methyl readers (HMRs), and DNMT3; limited funding allocated to the studies of such ‘dark’ targets could be a contributing factor [15].”
BackgroundTissue-specific genes as an underutilized resource in drug discovery
Ryaboshapkina, M., & Hammar, M. (2019) · Scientific Reports · doi:10.1038/s41598-019-43829-9 · Cited by 53 · see citing works
Drug design targeting active posttranslational modification protein isoforms
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“Statistical analysis suggested that only ~7% of biological space has been covered by small molecule probes, highlighting the fact that the biologically relevant chemical space is limited and poorly explicated.(26,36) There are two factors contributing to the small biologically relevant chemical space, one is small ove…”
BackgroundCeraudo, E., Horioka-Duplix, M., Mattheisen, J. M., Hitchman, T. D., Moore, A. R., Kazmi, M. A., Chi, P., Chen, Y., Sakmar, T. P., & Huber, T. (2020) · Journal of Biological Chemistry · doi:10.1074/jbc.ra120.015352 · Cited by 53 · see citing works
Yes SIR! On the structure–inactivity relationships in drug discovery
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Grand Challenges of Computer-Aided Drug Design: The Road Ahead
Medina‐Franco, J. L. (2021) · Frontiers in Drug Discovery · doi:10.3389/fddsv.2021.728551 · Cited by 57 · see citing works
“Identi fi cation of the therapeutically relevant targets has been the subject of recent comprehensive analysis (Oprea et al., 2018).”
BackgroundAn open invitation to the Understudied Proteins Initiative
Kustatscher, G., Collins, T., Gingras, A., Guo, T., Hermjakob, H., Ideker, T. E., Lilley, K. S., Lundberg, E. K., Marcotte, E. M., Ralser, M., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01316-z · Cited by 46 · see citing works
Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence
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Efficient Ligand Discovery Using Sulfur(VI) Fluoride Reactive Fragments
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How to Illuminate the Druggable Genome Using Pharos
Sheils, T. K., Mathias, S. L., Siramshetty, V. B., Bocci, G., Bologa, C., Yang, J. J., Waller, A., Southall, N. T., Nguyễn, Ð.-T., & Oprea, T. I. (2020) · Current Protocols in Bioinformatics · doi:10.1002/cpbi.92 · Cited by 45 · see citing works
“All targets in Pharos are categorized according to their “target development level” (TDL), a knowledge-based classification for human proteins (Oprea, Bologa, et al., 2018) that can be used to explore the “dark” genome (Oprea, 2019).”
BackgroundInfluentialSelf-citationHow many kinases are druggable? A review of our current understanding
Anderson, B. M., Rosston, P., Ong, H. W., Hossain, M. A., Davis‐Gilbert, Z. W., & Drewry, D. H. (2023) · Biochemical Journal · doi:10.1042/bcj20220217 · Cited by 49 · see citing works
Plasma Proteomics to Identify Drug Targets for Ischemic Heart Disease
Mazidi, M., Wright, N., Yao, P., Kartsonaki, C., Millwood, I. Y., Fry, H., Said, S., Pozarickij, A., Pei, P., Chen, Y., et al. (2023) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2023.09.804 · Cited by 49 · see citing works
TCMSID: a simplified integrated database for drug discovery from traditional chinese medicine
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“2c) [31].”
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Advances in mouse genetics for the study of human disease
Brown, S. D. M. (2021) · Human Molecular Genetics · doi:10.1093/hmg/ddab153 · Cited by 43 · see citing works
Identifying cross-disease components of genetic risk across hospital data in the UK Biobank
Cortés, A., Albers, P. K., Dendrou, C. A., Fugger, L. H., & McVean, G. (2019) · Nature Genetics · doi:10.1038/s41588-019-0550-4 · Cited by 43 · see citing works
Pharmacological enrichment of polygenic risk for precision medicine in complex disorders
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Covalent binders in drug discovery
Vasudevan, A., Argiriadi, M. A., Baranczak, A., Friedman, M., Gavrilyuk, J. I., Hobson, A. D., Hulce, J. J., Osman, S., & Wilson, N. S. (2019) · Progress in medicinal chemistry · doi:10.1016/bs.pmch.2018.12.002 · Cited by 42 · see citing works
Kinase-targeting small-molecule inhibitors and emerging bifunctional molecules
Goebel, G. L., Qiu, X., & Wu, P. (2022) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2022.04.006 · Cited by 42 · see citing works
Sutherland, J. J., Yonchev, D., Fekete, A., & Urbán, L. (2023) · Nature Communications · doi:10.1038/s41467-023-40064-9 · Cited by 41 · see citing works
“To interpret these results in the context of marketed drug safety pro fi les, we tested 1958 unique drug substances across 200 safety pharmacology assays, resulting in 147 653 concentration-response curves (Supplementary Data 1 – 3).”
InfluentialDevelopment and biological applications of sulfur–triazole exchange (SuTEx) chemistry
Borne, A. L., Brulet, J. W., Yuan, K., & Hsu, K. (2021) · RSC Chemical Biology · doi:10.1039/d0cb00180e · Cited by 42 · see citing works
In silico off-target profiling for enhanced drug safety assessment
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“To classify compounds under a specific target as active or inactive, we applied the threshold settings introduced in the Illuminating the Druggable Genome (IDG) project (Supporting Information Table S3) 38,39 .”
Bajorath, J., Chávez-Hernández, A. L., Duran-Frigola, M., Fernández-de Gortari, E., Gasteiger, J., López-López, E., Maggiora, G. M., Medina-Franco, J. L., Méndez-Lucio, O., Mestres, J., et al. (2022) · Journal of Cheminformatics · doi:10.1186/s13321-022-00661-0 · Cited by 38 · see citing works
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“under investigation are well known for targeting EGFR, VEGFR, PI3K, and mTOR [18].”
BackgroundCellular response to small molecules that selectively stall protein synthesis by the ribosome
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“4 INTRODUCTION Proteins are the main druggable therapeutic targets for the treatment of human diseases, ranging from metabolic disease, to cancer and dementia [1].”
Current and Future Challenges in Modern Drug Discovery
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Illuminating the druggable genome: Pathways to progress
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COVID-19 research risks ignoring important host genes due to pre-established research patterns
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“Future efforts in drug discovery should thus bene fi t from an in-depth analysis of protein states, which may allow for tapping into a larger chemical space for interfering with complex biological systems and thereby expanding the druggable genome 43 .”
BackgroundStructural biology and structure–function relationships of membrane proteins
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Moving targets in drug discovery
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Self-citation
- Authors
- Oprea, T. I., Bologa, C. G., Brunak, S., Campbell, A., Gan, G. N., Gaulton, A., Gomez, S. M., Guha, R., Hersey, A., Holmes, J., Jadhav, A., Jensen, L. J., Johnson, G. L., Karlson, A., Leach, A. R., Ma'ayan, A., Malovannaya, A., Mani, S., Mathias, S. L., McManus, M. T., Meehan, T. F., von Mering, C., Muthas, D., Nguyen, D.-T., Overington, J. P., Papadatos, G., Qin, J., Reich, C., Roth, B. L., Schürer, S. C., Simeonov, A., Sklar, L. A., Southall, N., Tomita, S., Tudose, I., Ursu, O., Vidović, D., Waller, A., Westergaard, D., Yang, J. J., & Zahoránszky-Köhalmi, G.
- Affiliations
- University of Copenhagen (DK)
- University of New Mexico (US)
- IQVIA (United States) (US)
- UNM Comprehensive Cancer Center (US)
- European Bioinformatics Institute (GB)
- University of North Carolina at Chapel Hill (US)
- National Institutes of Health (US)
- Baylor College of Medicine (US)
- University of California, San Francisco (US)
- AstraZeneca (Sweden) (SE)
- University of Miami (US)
- Yale University (US)
- Corresponding author
- Oprea, Tudor I.
- ORCID
- Oprea, Tudor I.
- Bologa, Cristian G.
- Brunak, Søren
- Gan, Gregory N.
- Gaulton, Anna
- Gomez, Shawn M.
- Guha, Rajarshi
- Hersey, Anne
- Holmes, Jayme
- Jadhav, Ajit
- Jensen, Lars Juhl
- Johnson, Gary L.
- Leach, Andrew R.
- Malovannaya, Anna
- Mathias, Stephen L.
- McManus, Michael T.
- Meehan, Terrence F.
- Muthas, Daniel
- Overington, John P.
- Papadatos, George
- Qin, Jun
- Reich, Christian
- Roth, Bryan L.
- Schürer, Stephan C.
- Simeonov, Anton
- Sklar, Larry A.
- Southall, Noel
- Tomita, Susumu
- Tudose, Ilinca
- Ursu, Oleg
- Waller, Anna
- Westergaard, David
- Yang, Jeremy J.
- Zahoránszky-Köhalmi, Gergely
- Venue
- Nature Reviews Drug Discovery
- Published
- 2018-03-23
- Volume
- 17
- Issue
- 5
- Pages
- 317-332
- ISSN
- 1474-1776 (ISSN-L)
- 1474-1784
- PMC
- PMC6339563
- OpenAlex
- W4294576509
- Cited by
- 378
- References
- 103
- Citations by year
- 2026: 34 · 2025: 36 · 2024: 48 · 2023: 57 · 2022: 44 · 2021: 60 · 2020: 46 · 2019: 37 · 2018: 16
- FWCI
- 3.12
- Type
- journal article
- Language
- en
- Update notices
- erratum (2018-03-23)
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Nature Reviews Drug Discovery (publishedVersion, cc-by-sa)
- PubMed (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, other-oa)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (submittedVersion)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, other-oa)
- Zurich Open Repository and Archive (University of Zurich)
- Funders
- University of Miami
- Wellcome Trust (WT086151, 104104/A/14/Z, WT104104/Z/14/Z)
- European Molecular Biology Laboratory (WT104104/Z/14/Z)
- Novo Nordisk (NNF14CC0001)
- Novo Nordisk Fonden (NNF 14CC0001)
- National Institutes of Health (U24TR002278, U24 224370, UL1TR001449, CA118100)
- National Institute of Mental Health (U01MH104984, U01MH104974, U01MH105028, U01MH104999, U01MH105026)
- National Human Genome Research Institute (UM1HG006370)
- National Cancer Institute (U24CA224260, R01CA177993, U54CA189201, U54CA189205, U24CA224370, P30CA118100, P50CA058223)
- National Institute of Diabetes and Digestive and Kidney Diseases (U24DK116195, U24DK116204, U24DK116214)
- National Center for Advancing Translational Sciences
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Health Sciences › Medicine › Reproductive Medicine › Science, Research, and Medicine
- Keywords
- druggable proteins, human proteome, G protein-coupled receptors, protein kinases, ion channels, therapeutic opportunities
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