The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Machine learning methods in chemoinformatics
Open accessAbstract
Machine learning algorithms are generally developed in computer science or adjacent disciplines and find their way into chemical modeling by a process of diffusion. Though particular machine learning methods are popular in chemoinformatics and quantitative structure–activity relationships (QSAR), many others exist in the technical literature. This discussion is methods‐based and focused on some algorithms that chemoinformatics researchers frequently use. It makes no claim to be exhaustive. We concentrate on methods for supervised learning, predicting the unknown property values of a test set of instances, usually molecules, based on the known values for a training set. Particularly relevant approaches include Artificial Neural Networks, Random Forest, Support Vector Machine, k‐Nearest Neighbors and naïve Bayes classifiers. WIREs Comput Mol Sci 2014, 4:468–481. This article is categorized under: Computer and Information Science > Chemoinformatics
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Cited by
Daina, A., Michielin, O., & Zoete, V. (2017) · Scientific Reports · doi:10.1038/srep42717 · Cited by 18,367
MoleculeNet: a benchmark for molecular machine learning
Wu, Z., Ramsundar, B., Feinberg, E. N., Gomes, J., Geniesse, C., Pappu, A. S., Leswing, K., & Pande, V. (2018) · Chemical Science · doi:10.1039/c7sc02664a · Cited by 3,107
Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks
Segler, M. H. S., Kogej, T., Tyrchan, C., & Waller, M. P. (2018) · ACS Central Science · doi:10.1021/acscentsci.7b00512 · Cited by 1,537
Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
Keith, J. A., Vassilev-Galindo, V., Cheng, B., Chmiela, S., Gastegger, M., Müller, K.-R., & Tkatchenko, A. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00107 · Cited by 849
Deep Dive into Plastic Monomers, Additives, and Processing Aids
Wiesinger, H., Wang, Z., & Hellweg, S. (2021) · Environmental Science & Technology · doi:10.1021/acs.est.1c00976 · Cited by 724
Metadata
- Authors
- Mitchell, J. B. O.
- Venue
- WIREs Computational Molecular Science
- Publisher
- Wiley
- Published
- 2014-02-24
- Volume
- 4
- Issue
- 5
- Pages
- 468-481
- ISSN
- 1759-0876 (ISSN-L)
- 1759-0884
- OpenAlex
- W2171830166
- Cited by
- 469
- References
- 7
- FWCI
- 25.12
- Type
- journal article
- Language
- en
- Open access
- hybrid
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