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Using Extended-Connectivity Fingerprints with Laplacian-Modified Bayesian Analysis in High-Throughput Screening Follow-Up
Open accessAbstract
This article describes the use of a combination of extended-connectivity fingerprints (ECFPs) and Laplacian-modified Bayesian analysis in a study of the inhibition of Escherichia coli dihydrofolate reductase. The McMaster High-Throughput Screening Lab at McMaster University proposed a competition to predict the hits in a separate test set of 50,000 compounds. Although the problem seemed best approached with 3D methods, the authors show that 2D methods offer surprisingly competitive results with a low computational cost.
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Cited by
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Besnard, J., Ruda, G. F., Setola, V., Abecassis, K., Rodriguiz, R. M., Huang, X.-P., Norval, S., Sassano, M. F., Shin, A. I., Webster, L. A., et al. (2012) · Nature · doi:10.1038/nature11691 · Cited by 832
Artificial Intelligence for Drug Discovery: Are We There Yet?
Hasselgren, C., & Oprea, T. I. (2024) · Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-040323-040828 · Cited by 236
“It uses a Laplacian-modified (105) Bayesian classifier (106) trained for polypharmacology profiling and employs a vector”
MethodOpen Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery Datasets
Clark, A. M., Dole, K., Coulon-Spektor, A., McNutt, A., Grass, G., Freundlich, J. S., Reynolds, R. C., & Ekins, S. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00143 · Cited by 114
Wang, L., Tang, Y., Liu, S., Mao, S., Ling, Y., Liu, D., He, X., & Wang, X. (2013) · PLoS ONE · doi:10.1371/journal.pone.0065675 · Cited by 97
“, constructing models repeatedly by leaving out one-tenth of the samples and predicting them back into the model) [38].”
BackgroundMachine Learning Models and Pathway Genome Data Base for Trypanosoma cruzi Drug Discovery
Ekins, S., Lage de Siqueira-Neto, J., McCall, L.-I., Sarker, M., Yadav, M., Ponder, E. L., Kallel, E. A., Kellar, D., Chen, S., Arkin, M., et al. (2015) · PLOS Neglected Tropical Diseases · doi:10.1371/journal.pntd.0003878 · Cited by 93
Metadata
- Authors
- Rogers, D., Brown, R. D., & Hahn, M.
- Venue
- SLAS Discovery
- Publisher
- Elsevier BV
- Published
- 2005-10
- Volume
- 10
- Issue
- 7
- Pages
- 682-686
- ISSN
- 2472-5552 (ISSN-L)
- OpenAlex
- W2142602210
- Cited by
- 296
- References
- 0
- FWCI
- 10.98
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Rogers, D., Brown, R. D., & Hahn, M. (2005). Using Extended-Connectivity Fingerprints with Laplacian-Modified Bayesian Analysis in High-Throughput Screening Follow-Up. SLAS Discovery. https://doi.org/10.1177/1087057105281365
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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