Machine learning methods in chemoinformatics
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Date
24/02/2014Author
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Grant ID
BB/I00596X/1
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Show full item recordAbstract
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.
Citation
Mitchell , J B O 2014 , ' Machine learning methods in chemoinformatics ' , Wiley Interdisciplinary Reviews: Computational Molecular Science , vol. 4 , no. 5 , pp. 468–481 . https://doi.org/10.1002/wcms.1183
Publication
Wiley Interdisciplinary Reviews: Computational Molecular Science
Status
Peer reviewed
ISSN
1759-0876Type
Journal article
Rights
© 2014 The Authors. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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