期刊
WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE
卷 4, 期 5, 页码 468-481出版社
WILEY
DOI: 10.1002/wcms.1183
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资金
- Scottish Universities Life Sciences Alliance (SULSA)
- BBSRC [BB/I00596X/1]
- BBSRC [BB/I00596X/1] Funding Source: UKRI
- Biotechnology and Biological Sciences Research Council [BB/I00596X/1] Funding Source: researchfish
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 naive Bayes classifiers. (C) 2014 The Authors. WIREs Computational Molecular Science published by JohnWiley & Sons, Ltd.
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