The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Lessons Learnt from Assembling Screening Libraries for Drug Discovery for Neglected Diseases
Open accessAbstract
To enable the establishment of a drug discovery operation for neglected diseases, out of 2.3 million commercially available compounds 222 552 compounds were selected for an in silico library, 57 438 for a diverse general screening library, and 1 697 compounds for a focused kinase set. Compiling these libraries required a robust strategy for compound selection. Rules for unwanted groups were defined and selection criteria to enrich for lead‐like compounds which facilitate straightforward structure–activity relationship exploration were established. Further, a literature and patent review was undertaken to extract key recognition elements of kinase inhibitors (“core fragments”) to assemble a focused library for hit discovery for kinases. Computational and experimental characterisation of the general screening library revealed that the selected compounds 1) span a broad range of lead‐like space, 2) show a high degree of structural integrity and purity, and 3) demonstrate appropriate solubility for the purposes of biochemical screening. The implications of this study for compound selection, especially in an academic environment with limited resources, are considered.
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
Can the pharmaceutical industry reduce attrition rates?
Kola, I., & Landis, J. (2004) · Nature Reviews Drug Discovery · doi:10.1038/nrd1470 · Cited by 4,167
Drugs for bad bugs: confronting the challenges of antibacterial discovery
Payne, D. J., Gwynn, M. N., Holmes, D. J., & Pompliano, D. L. (2007) · Nature Reviews Drug Discovery · doi:10.1038/nrd2201 · Cited by 2,512
Protein kinases — the major drug targets of the twenty-first century?
Cohen, P. (2002) · Nature Reviews Drug Discovery · doi:10.1038/nrd773 · Cited by 2,186
A ‘Rule of Three’ for fragment-based lead discovery?
Congreve, M., Carr, R., Murray, C., & Jhoti, H. (2003) · Drug Discovery Today · doi:10.1016/s1359-6446(03)02831-9 · Cited by 1,354
A decade of fragment-based drug design: strategic advances and lessons learned
Hajduk, P. J., & Greer, J. (2007) · Nature Reviews Drug Discovery · doi:10.1038/nrd2220 · Cited by 1,064
Hit and lead generation: beyond high-throughput screening
Bleicher, K. H., Böhm, H.-J., Müller, K., & Alanine, A. I. (2003) · Nature Reviews Drug Discovery · doi:10.1038/nrd1086 · Cited by 1,056
Integration of virtual and high-throughput screening
Bajorath, J. (2002) · Nature Reviews Drug Discovery · doi:10.1038/nrd941 · Cited by 930
Drug development for neglected diseases: a deficient market and a public-health policy failure
Trouiller, P., Olliaro, P., Torreele, E., Orbinski, J., Laing, R., & Ford, N. (2002) · The Lancet · doi:10.1016/s0140-6736(02)09096-7 · Cited by 893
Cited by
Daina, A., Michielin, O., & Zoete, V. (2017) · Scientific Reports · doi:10.1038/srep42717 · Cited by 18,367
A novel multiple-stage antimalarial agent that inhibits protein synthesis
Baragaña, B., Hallyburton, I., Lee, M. C. S., Norcross, N. R., Grimaldi, R., Otto, T. D., Proto, W. R., Blagborough, A. M., Meister, S., Wirjanata, G., et al. (2015) · Nature · doi:10.1038/nature14451 · Cited by 454
Self-citationDrug repositioning for orphan diseases
Sardana, D., Zhu, C., Zhang, M., Gudivada, R. C., Yang, L., & Jegga, A. G. (2011) · Briefings in Bioinformatics · doi:10.1093/bib/bbr021 · Cited by 228
De Novo Design of New Chemical Entities for SARS-CoV-2 Using Artificial Intelligence
Bung, N., Krishnan, S. R., Bulusu, G., & Roy, A. (2021) · Future Medicinal Chemistry · doi:10.4155/fmc-2020-0262 · Cited by 118
Discovering small-molecule senolytics with deep neural networks
Wong, F., Omori, S., Donghia, N. M., Zheng, E. J., & Collins, J. J. (2023) · Nature Aging · doi:10.1038/s43587-023-00415-z · Cited by 82
Metadata
- Authors
- Brenk, R., Schipani, A., James, D., Krasowski, A., Gilbert, I. H., Frearson, J., & Wyatt, P. G.
- Venue
- ChemMedChem
- Publisher
- Wiley
- Published
- 2008-03-07
- Volume
- 3
- Issue
- 3
- Pages
- 435-444
- ISSN
- 1860-7179 (ISSN-L)
- 1860-7187
- OpenAlex
- W2113364934
- Cited by
- 709
- References
- 40
- FWCI
- 7.23
- Type
- journal article
- Language
- en
- Open access
- green
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: Lessons Learnt from Assembling Screening Libraries for Drug Discovery for Neglected Diseases. https://app.cochat.ai/papers/10.1002/cmdc.200700139
- The paper
- Brenk, R., Schipani, A., James, D., Krasowski, A., Gilbert, I. H., Frearson, J., & Wyatt, P. G. (2008). Lessons Learnt from Assembling Screening Libraries for Drug Discovery for Neglected Diseases. ChemMedChem. https://doi.org/10.1002/cmdc.200700139
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.
