The DOI matches its Crossref record; 71 of the 71 references it cites were found in a registry or an index.
Constrained Bayesian optimization for automatic chemical design using variational autoencoders
Open accessAbstract
Automatic Chemical Design is a framework for generating novel molecules with optimized properties.
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Cites
Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067
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Advances in neural information processing systems 7
(1996) · Computers & Mathematics with Applications · doi:10.1016/s0898-1221(96)90463-0 · Cited by 14,281
Advances in Neural Information Processing Systems 29
Teymur, O., Zygalakis, K., & Calderhead, B. (2016) · Cited by 13,881
Proceedings of the 24th international conference on Machine learning
(2007) · doi:10.1145/1273496 · Cited by 11,630
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
Extended-Connectivity Fingerprints
Rogers, D., & Hahn, M. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci100050t · Cited by 7,524
Weininger, D. (1988) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci00057a005 · Cited by 7,436
ChEMBL: a large-scale bioactivity database for drug discovery
Gaulton, A., Bellis, L. J., Bento, A. P., Chambers, J., Davies, M., Hersey, A., Light, Y., McGlinchey, S., Michalovich, D., Al-Lazikani, B., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gkr777 · Cited by 4,382
Baell, J. B., & Holloway, G. A. (2010) · Journal of Medicinal Chemistry · doi:10.1021/jm901137j · Cited by 3,944
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AI in drug discovery and its clinical relevance
Qureshi, R., Irfan, M., Gondal, T. M., Khan, S., Wu, J., Hadi, M. U., Heymach, J., Le, X., Yan, H., & Alam, T. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e17575 · Cited by 319
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Wang, M., Hsieh, C.-Y., Wang, J., Wang, D., Weng, G., Shen, C., Yao, X., Bing, Z., Li, H., Cao, D., et al. (2022) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.2c00732 · Cited by 99
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Metadata
- Authors
- Griffiths, R.-R., & Hernández-Lobato, J. M.
- Venue
- Chemical Science
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2020
- Volume
- 11
- Issue
- 2
- Pages
- 577-586
- ISSN
- 2041-6520 (ISSN-L)
- 2041-6539
- OpenAlex
- W2985931096
- Cited by
- 339
- References
- 71
- FWCI
- 21.45
- Type
- journal article
- Language
- en
- Open access
- diamond
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- Griffiths, R.-R., & Hernández-Lobato, J. M. (2020). Constrained Bayesian optimization for automatic chemical design using variational autoencoders. Chemical Science. https://doi.org/10.1039/c9sc04026a
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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.
