The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Quantitative Structure–Property Relationship Modeling of Diverse Materials Properties
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Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Handbook of Molecular Descriptors
Todeschini, R., & Consonni, V. (2000) · Methods and Principles in Medicinal Chemistry · doi:10.1002/9783527613106 · Cited by 4,107
Cramer, R. D., Patterson, D. E., & Bunce, J. D. (1988) · Journal of the American Chemical Society · doi:10.1021/ja00226a005 · Cited by 3,931
Golbraikh, A., & Tropsha, A. (2002) · Journal of Molecular Graphics and Modelling · doi:10.1016/s1093-3263(01)00123-1 · Cited by 3,735
Combinatorial Explosion in Homogeneous Catalysis: Screening 60,000 Cross‐Coupling Reactions
Burello, E., Farrusseng, D., & Rothenberg, G. (2004) · Advanced Synthesis & Catalysis · doi:10.1002/adsc.200404170 · Cited by 75
Cited by
Deep learning for computational chemistry
Goh, G. B., Hodas, N. O., & Vishnu, A. (2017) · Journal of Computational Chemistry · doi:10.1002/jcc.24764 · Cited by 809
Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning
Lu, S., Zhou, Q., Ouyang, Y., Guo, Y., Li, Q., & Wang, J. (2018) · Nature Communications · doi:10.1038/s41467-018-05761-w · Cited by 725
Machine Learning for Catalysis Informatics: Recent Applications and Prospects
Toyao, T., Maeno, Z., Takakusagi, S., Kamachi, T., Takigawa, I., & Shimizu, K.-I. (2020) · ACS Catalysis · doi:10.1021/acscatal.9b04186 · Cited by 617
Koutsoukas, A., Monaghan, K. J., Li, X., & Huan, J. (2017) · Journal of Cheminformatics · doi:10.1186/s13321-017-0226-y · Cited by 315
“Applications range from quantitative structure–property relationships (QSPRs) [1–3], quantitative structure–activity relationships (QSARs) [4, 5] to in silico mode-of-action analysis and predictive toxicology [6, 7].”
Machine learning meets volcano plots: computational discovery of cross-coupling catalysts
Meyer, B., Sawatlon, B., Heinen, S., von Lilienfeld, O. A., & Corminboeuf, C. (2018) · Chemical Science · doi:10.1039/c8sc01949e · Cited by 209
Metadata
- Authors
- Le, T., Epa, V. C., Burden, F. R., & Winkler, D. A.
- Venue
- Chemical Reviews
- Publisher
- American Chemical Society (ACS)
- Published
- 2012-01-17
- Volume
- 112
- Issue
- 5
- Pages
- 2889-2919
- ISSN
- 0009-2665 (ISSN-L)
- 1520-6890
- OpenAlex
- W2315837940
- Cited by
- 565
- References
- 5
- FWCI
- 1.07
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Quantitative Structure–Property Relationship Modeling of Diverse Materials Properties. https://app.cochat.ai/papers/10.1021/cr200066h
- The paper
- Le, T., Epa, V. C., Burden, F. R., & Winkler, D. A. (2012). Quantitative Structure–Property Relationship Modeling of Diverse Materials Properties. Chemical Reviews. https://doi.org/10.1021/cr200066h
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.
