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Machine learning methods in chemoinformatics
Author(s) -
Mitchell John B. O.
Publication year - 2014
Publication title -
wiley interdisciplinary reviews: computational molecular science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.126
H-Index - 81
eISSN - 1759-0884
pISSN - 1759-0876
DOI - 10.1002/wcms.1183
Subject(s) - cheminformatics , machine learning , artificial intelligence , computer science , quantitative structure–activity relationship , naive bayes classifier , random forest , set (abstract data type) , artificial neural network , test set , process (computing) , support vector machine , data mining , chemistry , computational chemistry , operating system , programming language
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