The DOI matches its Crossref record.
Trends in clinical success rates and therapeutic focus
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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?
In the citing paper
“Human genetics can provide compelling evidence of causation through approaches such as Mendelian randomization (MR).”
From Integrating genomics with biomarkers and therapeutic targets to invigorate cardiovascular drug development (2021) · cites this paper as [9]
In the citing paper
“Human genetics can provide a tool by which the causal role of an exposure (such as a complex phenotype and/or a drug target) can be characterized 9 .”
From Integrating genomics with biomarkers and therapeutic targets to invigorate cardiovascular drug development (2021) · cites this paper as [9]
In the citing paper
“Performed well, such studies called Mendelian randomization (MR) analyses, can provide rigorous evidence of causation 9,10 .”
From Integrating genomics with biomarkers and therapeutic targets to invigorate cardiovascular drug development (2021) · cites this paper as [9]
Citations
Cited by
Sorted by citations
Molecular Docking: Shifting Paradigms in Drug Discovery
Pinzi, L., & Rastelli, G. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20184331 · Cited by 2,537
“Considering the high attrition rates characterizing drug discovery [203,204], the possibilities offered by docking in combination with the approaches outlined here will be important to reduce time and costs in both the development of clinical candidates with better safety profiles (target profiling and ADRs findings)…”
BackgroundWhy 90% of clinical drug development fails and how to improve it?
Sun, D., Gao, W., Hu, H., & Zhou, S. (2022) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2022.02.002 · Cited by 1,763
“The “rule of 5” have been considered in chemical structure design: (1) less than 5 H-bond donors; (2) molecular weight less than 500; (3) cLogP less than 5; (4) less than 10 H-bond acceptors(31).”
BackgroundMethodInfluentialFu, L., Shi, S., Yi, J., Wang, N., He, Y., Wu, Z., Peng, J., Deng, Y., Wang, W., Wu, C., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae236 · Cited by 1,396
“Addition to them, toxicity, another important evaluation indicator in the drug development process, also accounts for up to 30% of drug development failures ( 2 ,4 ).”
Advancing human genetics research and drug discovery through exome sequencing of the UK Biobank
Szustakowski, J. D., Balasubramanian, S., Kvikstad, E., Khalid, S., Bronson, P. G., Sasson, A., Wong, E., Liu, D., Wade Davis, J., Haefliger, C., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00885-0 · Cited by 401
Transfer Learning for Drug Discovery
Cai, C., Wang, S., Xu, Y., Zhang, W., Tang, K., Ouyang, Q., Lai, L., & Pei, J. (2020) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.9b02147 · Cited by 376
Metadata
- Authors
- Dowden, H., & Munro, J.
- Corresponding author
- Munro, Jamie
- Venue
- Nature Reviews Drug Discovery
- Published
- 2019-05-08
- Volume
- 18
- Issue
- 7
- Pages
- 495-496
- ISSN
- 1474-1776 (ISSN-L)
- 1474-1784
- OpenAlex
- W2944466104
- Cited by
- 649
- References
- 0
- Citations by year
- 2026: 101 · 2025: 129 · 2024: 140 · 2023: 114 · 2022: 57 · 2021: 57 · 2020: 43 · 2019: 7
- FWCI
- 39.38
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nature Reviews Drug Discovery (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Science, Research, and Medicine
- Health Sciences › Medicine › Pharmacology (medical) › Historical Medical Research and Treatments
- Health Sciences › Medicine › Internal Medicine › General Medicine and Therapeutics
- Keywords
- clinical success rate, therapeutic focus, drug development, clinical trial results, therapeutic areas
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.
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