The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Network target for screening synergistic drug combinations with application to traditional Chinese medicine
Open accessAbstract
BACKGROUND: Multicomponent therapeutics offer bright prospects for the control of complex diseases in a synergistic manner. However, finding ways to screen the synergistic combinations from numerous pharmacological agents is still an ongoing challenge. RESULTS: In this work, we proposed for the first time a "network target"-based paradigm instead of the traditional "single target"-based paradigm for virtual screening and established an algorithm termed NIMS (Network target-based Identification of Multicomponent Synergy) to prioritize synergistic agent combinations in a high throughput way. NIMS treats a disease-specific biological network as a therapeutic target and assumes that the relationship among agents can be transferred to network interactions among the molecular level entities (targets or responsive gene products) of agents. Then, two parameters in NIMS, Topology Score and Agent Score, are created to evaluate the synergistic relationship between each given agent combinations. Taking the empirical multicomponent system traditional Chinese medicine (TCM) as an illustrative case, we applied NIMS to prioritize synergistic agent pairs from 63 agents on a pathological process instanced by angiogenesis. The NIMS outputs can not only recover five known synergistic agent pairs, but also obtain experimental verification for synergistic candidates combined with, for example, a herbal ingredient Sinomenine, which outperforms the meet/min method. The robustness of NIMS was also showed regarding the background networks, agent genes and topological parameters, respectively. Finally, we characterized the potential mechanisms of multicomponent synergy from a network target perspective. CONCLUSIONS: NIMS is a first-step computational approach towards identification of synergistic drug combinations at the molecular level. The network target-based approaches may adjust current virtual screen mode and provide a systematic paradigm for facilitating the development of multicomponent therapeutics as well as the modernization of TCM.
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
Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206
Angiogenesis in cancer, vascular, rheumatoid and other disease
Folkman, J. (1995) · Nature Medicine · doi:10.1038/nm0195-27 · Cited by 7,627
Multicomponent therapeutics for networked systems
Keith, C. T., Borisy, A. A., & Stockwell, B. R. (2005) · Nature Reviews Drug Discovery · doi:10.1038/nrd1609 · Cited by 769
Systems biology and combination therapy in the quest for clinical efficacy
Fitzgerald, J. B., Schoeberl, B., Nielsen, U. B., & Sorger, P. K. (2006) · Nature Chemical Biology · doi:10.1038/nchembio817 · Cited by 570
Cancer as a robust system: implications for anticancer therapy
Kitano, H. (2004) · Nature reviews. Cancer · doi:10.1038/nrc1300 · Cited by 456
Protein-protein interaction networks and biology—what's the connection?
Hakes, L., Pinney, J. W., Robertson, D. L., & Lovell, S. C. (2008) · Nature Biotechnology · doi:10.1038/nbt0108-69 · Cited by 200 · cited 1×
“Moreover, NIMS is also relatively robust to the background network, although available networks such as the PPI network are still incomplete and biased (Figure 3c) [54].”
Background
Cited by
Network Pharmacology Databases for Traditional Chinese Medicine: Review and Assessment
Zhang, R., Zhu, X., Bai, H., & Ning, K. (2019) · Frontiers in Pharmacology · doi:10.3389/fphar.2019.00123 · Cited by 1,257
Zhao, L., Zhang, H., Li, N., Chen, J., Xu, H., Wang, Y., & Liang, Q. (2023) · Journal of Ethnopharmacology · doi:10.1016/j.jep.2023.116306 · Cited by 963
Structure and dynamics of molecular networks: A novel paradigm of drug discovery
Csermely, P., Korcsmáros, T., Kiss, H. J. M., London, G., & Nussinov, R. (2013) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2013.01.016 · Cited by 892
Kibble, M., Saarinen, N., Tang, J., Wennerberg, K., Mäkelä, S., & Aittokallio, T. (2015) · Natural Product Reports · doi:10.1039/c5np00005j · Cited by 489
Liu, H., Wang, J., Zhou, W., Wang, Y., & Yang, L. (2013) · Journal of Ethnopharmacology · doi:10.1016/j.jep.2013.02.004 · Cited by 326
Metadata
- Authors
- Li, S., Zhang, B., & Zhang, N.
- Venue
- BMC Systems Biology
- Published
- 2011
- Volume
- 5
- Issue
- Suppl 1
- Pages
- S10
- ISSN
- 1752-0509 (ISSN-L)
- OpenAlex
- W2113551867
- Cited by
- 380
- References
- 6
- Type
- conference abstract
- Language
- en
- Open access
- gold
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 target for screening synergistic drug combinations with application to traditional Chinese medicine. https://app.cochat.ai/papers/10.1186/1752-0509-5-s1-s10
- The paper
- Li, S., Zhang, B., & Zhang, N. (2011). Network target for screening synergistic drug combinations with application to traditional Chinese medicine. BMC Systems Biology. https://doi.org/10.1186/1752-0509-5-s1-s10
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.