Trends in clinical success rates and therapeutic focus
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Human genetics can provide compelling evidence of causation through approaches such as Mendelian randomization.
“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]
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.
“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]
Performed well, Mendelian randomization analyses can provide rigorous evidence of causation.
“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]
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BackgroundWhy 90% of clinical drug development fails and how to improve it?
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“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 ).”
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“5,6 .”
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Artificial intelligence for small molecule anticancer drug discovery
Duo, L., Liu, Y., Ren, J., Tang, B., & Hirst, J. D. (2024) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2024.2367014 · Cited by 47 · see citing works
Reviving an R&D pipeline: a step change in the Phase II success rate
Wu, S. S., Fernando, K., Allerton, C. M. N., Jansen, K. U., Vincent, M. S., & Dolsten, M. (2020) · Drug Discovery Today · doi:10.1016/j.drudis.2020.10.019 · Cited by 47 · see citing works
Target Validation Using PROTACs: Applying the Four Pillars Framework
Nowak, R. P., & Jones, L. H. (2020) · SLAS DISCOVERY · doi:10.1177/2472555220979584 · Cited by 48 · see citing works
Flourishing tumor organoids: History, emerging technology, and application
Yang, Q., Li, M., Yang, X., Xiao, Z., Tong, X., Tuerdi, A., Li, S., & Lei, L. (2023) · Bioengineering & Translational Medicine · doi:10.1002/btm2.10559 · Cited by 45 · see citing works
“didates from phase I clinical trials to the initiation of late-stage development is below 10%, and the main reason for their failure is inadequate safety or efficacy.(179) Therefore, the establishment of”
BackgroundFernald, K., Förster, P. C., Claassen, E., & van de Burgwal, L. (2024) · Drug Discovery Today · doi:10.1016/j.drudis.2024.104160 · Cited by 51 · see citing works
Peng, Y., Wang, M., Xu, Y., et al. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa027 · Cited by 43 · see citing works
nach0: multimodal natural and chemical languages foundation model
Livne, M., Miftahutdinov, Z., Tutubalina, E. V., Kuznetsov, M., Polykovskiy, D. A., Brundyn, A., Jhunjhunwala, A., Costa, A. B., Aliper, A., Aspuru‐Guzik, A., et al. (2024) · Chemical Science · doi:10.1039/d4sc00966e · Cited by 50 · see citing works
“…for the biomedical domain, utilizing a variety of model families: for instance, researchers have developed BioBERT 17 , based on BERT with 110 million parameters, and SciFive, based on T5-base and T5-large with 220 and 770 million parameters respectively, using biomedical literature from PubMed.”
MethodSchuhmacher, A., Hinder, M., Brief, E., Gassmann, O., & Hartl, D. (2025) · Drug Discovery Today · doi:10.1016/j.drudis.2025.104291 · Cited by 43 · see citing works
Applications of Artificial Intelligence in Drug Design: Opportunities and Challenges
Thomas, M. C., Boardman, A., García-Ortegón, M., Yang, H., de Graaf, C., & Bender, A. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1787-8_1 · Cited by 41 · see citing works
Cohen, B. M., Lehnherr, D., Sezen-Edmonds, M., et al. (2023) · Process Safety and Environmental Protection · doi:10.1016/j.cherd.2023.02.050 · Cited by 42 · see citing works
Creemers, J. H. A., Ankan, A., Roes, K. C. B., Schröder, G., Mehra, N., Figdor, C. G., de Vries, I. J. M., & Textor, J. C. (2023) · Nature Communications · doi:10.1038/s41467-023-37933-8 · Cited by 39 · see citing works
“%) ultimately obtain market approval 2 – 4 .”
Prodrug Approach as a Strategy to Enhance Drug Permeability
de Souza, M. M., Gini, A. L. R., Moura, J. A., Scarim, C. B., Chin, C. M., & dos Santos, J. L. (2025) · Pharmaceuticals · doi:10.3390/ph18030297 · Cited by 43 · see citing works
“Studies indicate that 9 out of 10 approved drugs fail in demonstrating efficacy and safety during various stages of development [2].”
Microfluidic Liver-on-a-Chip for Preclinical Drug Discovery
Fu, J., Qiu, H., & Tan, C. S. (2023) · Pharmaceutics · doi:10.3390/pharmaceutics15041300 · Cited by 42 · see citing works
Cell-Type-Specific High Throughput Toxicity Testing in Human Midbrain Organoids
Renner, H., Becker, K., Kagermeier, T., Grabos, M., Eliat, F., Günther, P. S., Schöler, H. R., & Bruder, J. M. (2021) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2021.715054 · Cited by 40 · see citing works
The rat as an animal model in chronic wound research: An update
Ghanbari, M., Salkovskiy, Y., & Carlson, M. A. (2024) · Life Sciences · doi:10.1016/j.lfs.2024.122783 · Cited by 49 · see citing works
Yıldırım, Z., Swanson, K., Wu, X., Zou, J., & Wu, J. C. (2024) · The Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-022724-095035 · Cited by 39 · see citing works
Shamul, J. G., Wang, Z., Gong, H., Ou, W., White, A. M., Garcia, D. P. M., Gu, S., Clyne, A. M., Quiñones‐Hinojosa, A., & He, X. (2024) · Nature Biomedical Engineering · doi:10.1038/s41551-024-01250-2 · Cited by 42 · see citing works
“Animal models are currently used widely for preclinical testing of drug transport across the BBB and efficacy in the brain; however, better models are needed since ~92% of drug candidates that show success in animal models fail in clinical trials 98–100 .”
Meta-MolNet: A Cross-Domain Benchmark for Few Examples Drug Discovery
Lv, Q., Chen, G., Yang, Z., Zhong, W., & Chen, C. Y. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3359657 · Cited by 38 · see citing works
“T HE discovery of new drugs is a system engineering with high investment, long cycle, and high risk [1], [2].”
Clinical trials in-a-dish for cardiovascular medicine
Wu, X., Swanson, K., Yıldırım, Z., Liu, W., Liao, R., & Wu, J. C. (2024) · European Heart Journal · doi:10.1093/eurheartj/ehae519 · Cited by 40 · see citing works
- 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
- Health Sciences › Medicine › Reproductive Medicine › Science, Research, and Medicine
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Historical Medical Research and Treatments
- Health Sciences › Medicine › Surgery › Medical research and treatments
- 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.
What counts as a claim ›How we measure confidence ›How we check this ›
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