Machine learning methods in chemoinformatics
MetadataShow full item record
Altmetrics Handle Statistics
Altmetrics DOI Statistics
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.
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
Wiley Interdisciplinary Reviews: Computational Molecular Science
© 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.
Items in the St Andrews Research Repository are protected by copyright, with all rights reserved, unless otherwise indicated.