Machine learning methods in chemoinformatics | Zendy

Mitchell John B. O. | Zendy

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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 , random forest , naive bayes classifier , artificial neural network , set (abstract data type) , process (computing) , test set , support vector machine , chemistry , computational chemistry , programming language , operating system

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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