Machine learning methods in chemoinformatics

Wiley Interdisciplinary Reviews. Computational Molecular Science
John B O Mitchell

Abstract

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.

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Citations

Jan 16, 2016·Journal of Chemical Information and Modeling·Aaron Terrence Frank
Apr 26, 2016·Wiley Interdisciplinary Reviews. Computational Molecular Science·Qurrat Ul AinPedro J Ballester
Apr 29, 2015·Journal of Chemical Information and Modeling·Mateusz MaciejewskiEugen Lounkine
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Jul 15, 2017·Current Medicinal Chemistry·Jelica VucicevicJohn B O Mitchell

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