The DOI matches its Crossref record.
Strategic Pooling of Compounds for High-Throughput Screening
Abstract
Bringing new medicines to the market depends on the rapid discovery of new and effective drugs, often initiated through the biological testing of many thousands of compounds in high-throughput screening (HTS). Mixing compounds together into pools for screening is one way to accelerate this process and reduce costs. This paper contains both theoretical and experimental data which suggest that careful selection of compounds to be pooled together is necessary in order to reduce the risk of reactivity between compounds within the pools.
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Citations
Cited by
Gaulton, A., Hersey, A., Nowotka, M., Bento, A. P., Chambers, J., Mendez, D., Mutowo, P., Atkinson, F., Bellis, L. J., Cibrián-Uhalte, E., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw1074 · Cited by 2,330
Simple Selection Criteria for Drug-like Chemical Matter
Muegge, I., Heald, S. L., & Brittelli, D. (2001) · Journal of Medicinal Chemistry · doi:10.1021/jm015507e · Cited by 1,315
Counting on natural products for drug design
Rodrigues, T., Reker, D., Schneider, P., & Schneider, G. (2016) · Nature Chemistry · doi:10.1038/nchem.2479 · Cited by 1,250
Integration of virtual and high-throughput screening
Bajorath, J. (2002) · Nature Reviews Drug Discovery · doi:10.1038/nrd941 · Cited by 930
Lessons Learnt from Assembling Screening Libraries for Drug Discovery for Neglected Diseases
Brenk, R., Schipani, A., James, D., Krasowski, A., Gilbert, I. H., Frearson, J., & Wyatt, P. G. (2008) · ChemMedChem · doi:10.1002/cmdc.200700139 · Cited by 709
Derivation and Validation of Toxicophores for Mutagenicity Prediction
Kazius, J., McGuire, R., & Bursi, R. (2005) · Journal of Medicinal Chemistry · doi:10.1021/jm040835a · Cited by 666
Computational methods for the prediction of ‘drug-likeness’
Clark, D. E., & Pickett, S. D. (2000) · Drug Discovery Today · doi:10.1016/s1359-6446(99)01451-8 · Cited by 578
Constrained Bayesian optimization for automatic chemical design using variational autoencoders
Griffiths, R.-R., & Hernández-Lobato, J. M. (2020) · Chemical Science · doi:10.1039/c9sc04026a · Cited by 339
McGovern, S. L., & Shoichet, B. K. (2003) · Journal of Medicinal Chemistry · doi:10.1021/jm0300330 · Cited by 276
FAF-Drugs2: Free ADME/tox filtering tool to assist drug discovery and chemical biology projects
Lagorce, D., Sperandio, O., Galons, H., Miteva, M. A., & Villoutreix, B. O. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-396 · Cited by 263
DOGS: Reaction-Driven de novo Design of Bioactive Compounds
Hartenfeller, M., Zettl, H., Walter, M., Rupp, M., Reisen, F., Proschak, E., Weggen, S., Stark, H., & Schneider, G. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002380 · Cited by 251
“The set of unwanted substructures is based on definitions described by Hann et al. [1].”
MethodTowards reproducible computational drug discovery
Schaduangrat, N., Lampa, S., Simeon, S., Gleeson, M. P., Spjuth, O., & Nantasenamat, C. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-020-0408-x · Cited by 208
Badapple: promiscuity patterns from noisy evidence
Yang, J. J., Ursu, O., Lipinski, C. A., Sklar, L. A., Oprea, T. I., & Bologa, C. G. (2016) · Journal of Cheminformatics · doi:10.1186/s13321-016-0137-3 · Cited by 103
Jamal, S., Goyal, S., Shanker, A., & Grover, A. (2015) · PLOS ONE · doi:10.1371/journal.pone.0129370 · Cited by 19
Metadata
- Authors
- Hann, M., Hudson, B., Lewell, X., Lifely, R., Miller, L., & Ramsden, N.
- Venue
- Journal of Chemical Information and Computer Sciences
- Publisher
- American Chemical Society (ACS)
- Published
- 1999-07-17
- Volume
- 39
- Issue
- 5
- Pages
- 897-902
- ISSN
- 0095-2338 (ISSN-L)
- 1520-5142
- OpenAlex
- W2085587362
- Cited by
- 225
- References
- 0
- FWCI
- 10.95
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
