The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
A Data-Driven Approach to Predicting Successes and Failures of Clinical Trials
Open accessAbstract
Over the past decade, the rate of drug attrition due to clinical trial failures has risen substantially. Unfortunately it is difficult to identify compounds that have unfavorable toxicity properties before conducting clinical trials. Inspired by the effective use of sabermetrics in predicting successful baseball players, we sought to use a similar "moneyball" approach that analyzes overlooked features to predict clinical toxicity. We introduce a new data-driven approach (PrOCTOR) that directly predicts the likelihood of toxicity in clinical trials. PrOCTOR integrates the properties of a compound's targets and its structure to provide a new measure, the PrOCTOR score. Drug target network connectivity and expression levels, along with molecular weight, were identified as important indicators of adverse clinical events. Our method provides a data-driven, broadly applicable strategy to identify drugs likely to possess manageable toxicity in clinical trials and will help drive the design of therapeutic agents with less toxicity.
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.
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
“while efficacy is as complex to predict6.”
From Exploiting machine learning for end-to-end drug discovery and development (2019) · cites this paper as [6]
In the citing paper
“Gayvert et al. performed classified toxicity prediction on FDA-approved drugs and drugs that had failed to pass toxicity-tests, with the RF supervised learning algorithm. The learning was from multiple sources: chemical structure characterizations, the median value of the expression of the drug targeted genes from the transcriptome of various tissues, the frequency or possibility of functional”
From Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis (2018) · cites this paper as [157]
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
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
HTSeq—a Python framework to work with high-throughput sequencing data
Anders, S., Pyl, P. T., & Huber, W. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btu638 · Cited by 21,464
Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (2001) · Advanced Drug Delivery Reviews · doi:10.1016/s0169-409x(00)00129-0 · Cited by 18,139
The igraph software package for complex network research
Csárdi, G., & Nepusz, T. (2006) · Medical Entomology and Zoology · Cited by 10,078
Molecular properties that influence the oral bioavailability of drug candidates
Veber, D. F., Johnson, S. R., Cheng, H.-Y., Smith, B. R., Ward, K. W., & Kopple, K. D. (2002) · Journal of Medicinal Chemistry · doi:10.1021/jm020017n · Cited by 8,420
Lead- and drug-like compounds: the rule-of-five revolution
Lipinski, C. A. (2004) · Drug Discovery Today Technologies · doi:10.1016/j.ddtec.2004.11.007 · Cited by 6,455
The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans
The GTEx Consortium, Ardlie, K. G., Deluca, D. S., Segrè, A. V., Sullivan, T. J., Young, T. R., Gelfand, E. T., Trowbridge, C. A., Maller, J. B., Tukiainen, T., et al. (2015) · Science · doi:10.1126/science.1262110 · Cited by 5,062
Effect of Rosiglitazone on the Risk of Myocardial Infarction and Death from Cardiovascular Causes
Nissen, S. E., & Wolski, K. (2007) · New England Journal of Medicine · doi:10.1056/nejmoa072761 · Cited by 4,675
Ghose, A. K., Viswanadhan, V. N., & Wendoloski, J. J. (1998) · Journal of Combinatorial Chemistry · doi:10.1021/cc9800071 · Cited by 3,095
Clinical development success rates for investigational drugs
Hay, M., Thomas, D. W., Craighead, J. L., Economides, C., & Rosenthal, J. (2014) · Nature Biotechnology · doi:10.1038/nbt.2786 · Cited by 2,536
Flagged first, then by citations
Cited by
Sorted by citations
Items 301–310 of 310
IFNIKB: a type I interferon database for antitumour immunity studies
Ma, F., Li, K., Yu, Y., Zhang, L., Zhang, L., Li, B., & Zhang, L. (2026) · Database · doi:10.1093/database/baag053
Estimating and predicting clinical trial outcomes in China
Zhao, C., Pang, T., & Lo, A. W. (2026) · Drug Discovery Today · doi:10.1016/j.drudis.2026.104802
Clinical trial success prediction from open registry text using classical machine learningPreprint
Doane, M. R. (2026) · Research Square · doi:10.21203/rs.3.rs-10809937/v1
Artificial intelligence-driven personalized medicine and biomarker discovery
Singh, S., Kaushik, A. C., & Sahi, S. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-40338-5.00007-x
Kumar, A., Kumar, P., Singh, N., Singh, P. K., & Singh, A. (2026) · The Journal of Precision Medicine Health and Disease · doi:10.1016/j.premed.2026.100061
Algorithmic Limits and Reliability: The Legal Knot
Rotino, G. (2026) · Law for professionals · doi:10.1007/978-3-032-38537-6_4
Topology-Guided Multi-Level Graph Learning: A Unified Framework for Molecular Property Prediction
Ali, H., Arshad, M., Iqbal, M. A., Alsawadi, M. S., & Alghannam, F. (2026) · Molecules · doi:10.3390/molecules31193436
Enhanced Multi-Branch Attention Network for Toxicity Risk Prediction in Late-Stage Drug Development
Ullah, I., Fatima, S., Haq, I. U., Jan, S., & Talha, M. (2026) · VAWKUM Transactions on Computer Sciences · doi:10.21015/vtcs.v14i2.2628
Artificial intelligence-driven advances in drug toxicity prediction
康艳蕾, He, P., Hu, J., Zhu, J., & Yu, C. (2026) · Computational Toxicology · doi:10.1016/j.comtox.2026.100461
AI Agents in Drug Discovery and Clinical Trials
Kotwal, J., & Sharma, A. K. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-8562-4.ch020
Metadata
- Authors
- Gayvert, K. M., Madhukar, N. S., & Elemento, O.
- Affiliations
- Memorial Sloan Kettering Cancer Center (US)
- Weill Cornell Medicine (US)
- Corresponding author
- Elemento, Olivier
- Venue
- Cell chemical biology
- Publisher
- Elsevier BV
- Published
- 2016-10
- Volume
- 23
- Issue
- 10
- Pages
- 1294-1301
- ISSN
- 2451-9448 (ISSN-L)
- 2451-9456
- PMC
- PMC5074862
- OpenAlex
- W2461620095
- Cited by
- 310
- References
- 30
- Citations by year
- 2026: 46 · 2025: 48 · 2024: 49 · 2023: 52 · 2022: 33 · 2021: 30 · 2020: 29 · 2019: 13 · 2018: 8 · 2017: 1 · 2016: 1
- FWCI
- 7.05
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell chemical biology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- CAREER grant from National Science Foundation (DB1054964)
- NIH (R01CA194547)
- Starr Cancer Foundation
- Institute for Computational Biomedicine
- PhRMA Foundation Pre Doctoral Informatics Fellowship
- Tri-Institutional Training Program in Computational Biology and Medicine
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Toxicology › Pharmacovigilance and Adverse Drug Reactions
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Keywords
- drug attrition, molecular weight, adverse clinical events, structure-property relationships, drug toxicity
- MeSH terms
- Clinical Trials as Topic, Humans, Models, Biological, Models, Molecular, Software, Likelihood Functions, Computational Biology, Drug Discovery, Drug-Related Side Effects and Adverse Reactions
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
