The DOI matches its Crossref record; 110 of the 110 references it cites were found in a registry or an index.
Network pharmacology: curing causal mechanisms instead of treating symptoms
Open accessAbstract
For complex diseases, most drugs are highly ineffective, and the success rate of drug discovery is in constant decline. While low quality, reproducibility issues, and translational irrelevance of most basic and preclinical research have contributed to this, the current organ-centricity of medicine and the 'one disease-one target-one drug' dogma obstruct innovation in the most profound manner. Systems and network medicine and their therapeutic arm, network pharmacology, revolutionize how we define, diagnose, treat, and, ideally, cure diseases. Descriptive disease phenotypes are replaced by endotypes defined by causal, multitarget signaling modules that also explain respective comorbidities. Precise and effective therapeutic intervention is achieved by synergistic multicompound network pharmacology and drug repurposing, obviating the need for drug discovery and speeding up clinical translation.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
KEGG: Kyoto Encyclopedia of Genes and Genomes
Kanehisa, M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.27 · Cited by 37,077
Tissue-based map of the human proteome
Uhlén, M., Fagerberg, L., Hallström, B. M., Lindskog, C., Oksvold, P., Mardinoglu, A., Sivertsson, Å., Kampf, C., Sjöstedt, E., Asplund, A., et al. (2015) · Science · doi:10.1126/science.1260419 · Cited by 15,286
Vogelstein, B., Papadopoulos, N., Velculescu, V. E., Zhou, S., Diaz, L. A., & Kinzler, K. W. (2013) · Science · doi:10.1126/science.1235122 · Cited by 7,863
Network pharmacology: the next paradigm in drug discovery
Hopkins, A. L. (2008) · Nature Chemical Biology · doi:10.1038/nchembio.118 · Cited by 4,622
Mutational landscape and significance across 12 major cancer types
Kandoth, C., McLellan, M. D., Vandin, F., Ye, K., Niu, B., Lu, C., Xie, M., Zhang, Q., McMichael, J. F., Wyczalkowski, M. A., et al. (2013) · Nature · doi:10.1038/nature12634 · Cited by 4,419
DrugBank: a comprehensive resource for in silico drug discovery and exploration
Wishart, D. S. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkj067 · Cited by 3,892
A Novel Paradigm for Heart Failure With Preserved Ejection Fraction
Paulus, W. J., & Tschöpe, C. (2013) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2013.02.092 · Cited by 3,564
Goh, K.-I., Cusick, M. E., Valle, D., Childs, B., Vidal, M., & Barabási, A.-L. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0701361104 · Cited by 3,242
Oncogenic Signaling Pathways in The Cancer Genome Atlas
Sanchez-Vega, F., Mina, M., Armenia, J., Chatila, W. K., Luna, A., La, K. C., Dimitriadoy, S., Liu, D. L., Kantheti, H. S., Saghafinia, S., et al. (2018) · Cell · doi:10.1016/j.cell.2018.03.035 · Cited by 3,201
Self-citationGenomic responses in mouse models poorly mimic human inflammatory diseases
Seok, J., Warren, H. S., Cuenca, A. G., Mindrinos, M. N., Baker, H. V., Xu, W., Richards, D. R., McDonald-Smith, G. P., Gao, H., Hennessy, L., et al. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1222878110 · Cited by 3,005
Cited by
Zhang, P., Zhang, D., Zhou, W., Wang, L., Wang, B., Zhang, T., & Li, S. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad518 · Cited by 675
AI-powered therapeutic target discovery
Pun, F. W., Ozerov, I. V., & Zhavoronkov, A. (2023) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2023.06.010 · Cited by 341
Network pharmacology approaches for research of Traditional Chinese Medicines
LI, X., LIU, Z., LIAO, J., CHEN, Q., LU, X., & FAN, X. (2023) · Chinese Journal of Natural Medicines · doi:10.1016/s1875-5364(23)60429-7 · Cited by 308
Metadata
- Authors
- Nogales, C., Mamdouh, Z. M., List, M., Kiel, C., Casas, A. I., & Schmidt, H. H. H. W.
- Venue
- Trends in Pharmacological Sciences
- Publisher
- Elsevier BV
- Published
- 2022-02
- Volume
- 43
- Issue
- 2
- Pages
- 136-150
- ISSN
- 0165-6147 (ISSN-L)
- OpenAlex
- W4200045218
- Cited by
- 1,201
- References
- 110
- FWCI
- 13.47
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Network pharmacology: curing causal mechanisms instead of treating symptoms. https://app.cochat.ai/papers/10.1016/j.tips.2021.11.004
- The paper
- Nogales, C., Mamdouh, Z. M., List, M., Kiel, C., Casas, A. I., & Schmidt, H. H. H. W. (2022). Network pharmacology: curing causal mechanisms instead of treating symptoms. Trends in Pharmacological Sciences. https://doi.org/10.1016/j.tips.2021.11.004
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
