The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Robust QSAR Models Using Bayesian Regularized Neural Networks
Abstract
We describe the use of Bayesian regularized artificial neural networks (BRANNs) in the development of QSAR models. These networks have the potential to solve a number of problems which arise in QSAR modeling such as: choice of model; robustness of model; choice of validation set; size of validation effort; and optimization of network architecture. The application of the methods to QSAR of compounds active at the benzodiazepine and muscarinic receptors is illustrated.
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Cites
p-σ-π Analysis. A Method for the Correlation of Biological Activity and Chemical Structure
Hansch, C., & Fujita, T. (1964) · Journal of the American Chemical Society · doi:10.1021/ja01062a035 · Cited by 2,313
Can We Learn To Distinguish between “Drug-like” and “Nondrug-like” Molecules?
Ajay, Walters, W. P., & Murcko, M. A. (1998) · Journal of Medicinal Chemistry · doi:10.1021/jm970666c · Cited by 484
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Goh, G. B., Hodas, N. O., & Vishnu, A. (2017) · Journal of Computational Chemistry · doi:10.1002/jcc.24764 · Cited by 809
Beware of R2: Simple, Unambiguous Assessment of the Prediction Accuracy of QSAR and QSPR Models
Alexander, D. L. J., Tropsha, A., & Winkler, D. A. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00206 · Cited by 745
Self-citationBayesian Regularization of Neural Networks
Burden, F., & Winkler, D. (2008) · Methods in Molecular Biology™ · doi:10.1007/978-1-60327-101-1_3 · Cited by 621
Self-citationMolecular similarity: a key technique in molecular informatics
Bender, A., & Glen, R. C. (2004) · Organic & Biomolecular Chemistry · doi:10.1039/b409813g · Cited by 614
Quantitative Structure–Property Relationship Modeling of Diverse Materials Properties
Le, T., Epa, V. C., Burden, F. R., & Winkler, D. A. (2012) · Chemical Reviews · doi:10.1021/cr200066h · Cited by 565
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Metadata
- Authors
- Burden, F. R., & Winkler, D. A.
- Venue
- Journal of Medicinal Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 1999-07-24
- Volume
- 42
- Issue
- 16
- Pages
- 3183-3187
- ISSN
- 0022-2623 (ISSN-L)
- 1520-4804
- OpenAlex
- W2046653925
- Cited by
- 241
- References
- 2
- FWCI
- 6.67
- Type
- journal article
- Language
- en
- Open access
- closed
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- Burden, F. R., & Winkler, D. A. (1999). Robust QSAR Models Using Bayesian Regularized Neural Networks. Journal of Medicinal Chemistry. https://doi.org/10.1021/jm980697n
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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.
