The DOI matches its Crossref record; 107 of the 107 references it cites were found in a registry or an index.
Chemical predictive modelling to improve compound quality
Open accessAbstract
The 'quality' of small-molecule drug candidates — encompassing aspects including their potency, selectivity and pharmacokinetic characteristics — is a key factor influencing the chances of success in clinical trials. Cumming and colleagues discuss the application of computational methods, particularly quantitative structure–activity relationships, in guiding the selection of higher-quality drug candidates, as well as cultural factors that may have affected their impact. The 'quality' of small-molecule drug candidates, encompassing aspects including their potency, selectivity and ADMET (absorption, distribution, metabolism, excretion and toxicity) characteristics, is a key factor influencing the chances of success in clinical trials. Importantly, such characteristics are under the control of chemists during the identification and optimization of lead compounds. Here, we discuss the application of computational methods, particularly quantitative structure–activity relationships (QSARs), in guiding the selection of higher-quality drug candidates, as well as cultural factors that may have affected their use and impact.
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
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
Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (1997) · Advanced Drug Delivery Reviews · doi:10.1016/s0169-409x(96)00423-1 · Cited by 11,385
Escape from Flatland: Increasing Saturation as an Approach to Improving Clinical Success
Lovering, F., Bikker, J., & Humblet, C. (2009) · Journal of Medicinal Chemistry · doi:10.1021/jm901241e · Cited by 4,495
Baell, J. B., & Holloway, G. A. (2010) · Journal of Medicinal Chemistry · doi:10.1021/jm901137j · Cited by 3,944
How to improve R&D productivity: the pharmaceutical industry's grand challenge
Paul, S. M., Mytelka, D. S., Dunwiddie, C. T., Persinger, C. C., Munos, B. H., Lindborg, S. R., & Schacht, A. L. (2010) · Nature Reviews Drug Discovery · doi:10.1038/nrd3078 · Cited by 3,566
The R Project in Statistical Computing
Ripley, B. D. (2001) · MSOR Connections · doi:10.11120/msor.2001.01010023 · Cited by 2,529
The influence of drug-like concepts on decision-making in medicinal chemistry
Leeson, P. D., & Springthorpe, B. (2007) · Nature Reviews Drug Discovery · doi:10.1038/nrd2445 · Cited by 2,403
Quantifying the chemical beauty of drugs
Bickerton, G. R., Paolini, G. V., Besnard, J., Muresan, S., & Hopkins, A. L. (2012) · Nature Chemistry · doi:10.1038/nchem.1243 · Cited by 2,242
Self-citationADMET in silico modelling: towards prediction paradise?
van de Waterbeemd, H., & Gifford, E. (2003) · Nature Reviews Drug Discovery · doi:10.1038/nrd1032 · Cited by 1,863
Virtual Computational Chemistry Laboratory – Design and Description
Tetko, I. V., Gasteiger, J., Todeschini, R., Mauri, A., Livingstone, D., Ertl, P., Palyulin, V. A., Radchenko, E. V., Zefirov, N. S., Makarenko, A. S., et al. (2005) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-005-8694-y · Cited by 1,500
Cited by
Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks
Segler, M. H. S., Kogej, T., Tyrchan, C., & Waller, M. P. (2018) · ACS Central Science · doi:10.1021/acscentsci.7b00512 · Cited by 1,537
Rethinking drug design in the artificial intelligence era
Schneider, P., Walters, W. P., Plowright, A. T., Sieroka, N., Listgarten, J., Goodnow, R. A., Fisher, J., Jansen, J. M., Duca, J. S., Rush, T. S., et al. (2020) · Nature Reviews Drug Discovery · doi:10.1038/s41573-019-0050-3 · Cited by 894
SLC Transporters as Therapeutic Targets: Emerging Opportunities
Lin, L., Yee, S. W., Kim, R. B., & Giacomini, K. M. (2015) · Nature Reviews Drug Discovery · doi:10.1038/nrd4626 · Cited by 835
When Quality Beats Quantity: Decision Theory, Drug Discovery, and the Reproducibility Crisis
Scannell, J. W., & Bosley, J. (2016) · PLOS ONE · doi:10.1371/journal.pone.0147215 · Cited by 279
FAF-Drugs3: a web server for compound property calculation and chemical library design
Lagorce, D., Sperandio, O., Baell, J. B., Miteva, M. A., & Villoutreix, B. O. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv353 · Cited by 272
Metadata
- Authors
- Cumming, J. G., Davis, A. M., Muresan, S., Haeberlein, M., & Chen, H.
- Venue
- Nature Reviews Drug Discovery
- Published
- 2013-11-29
- Volume
- 12
- Issue
- 12
- Pages
- 948-962
- ISSN
- 1474-1776 (ISSN-L)
- 1474-1784
- OpenAlex
- W2020859943
- Cited by
- 311
- References
- 107
- FWCI
- 2.00
- 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: Chemical predictive modelling to improve compound quality. https://app.cochat.ai/papers/10.1038/nrd4128
- The paper
- Cumming, J. G., Davis, A. M., Muresan, S., Haeberlein, M., & Chen, H. (2013). Chemical predictive modelling to improve compound quality. Nature Reviews Drug Discovery. https://doi.org/10.1038/nrd4128
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.
