Journal of chemical information and modeling. 2016 Jan 25;56(1):12-20. doi: 10.1021/acs.jcim.5b00332 Q15.32025

Feasibility of Active Machine Learning for Multiclass Compound Classification

多类化合物分类中主动机器学习的可行性研究翻译改进

Tobias Lang, Florian Flachsenberg, Ulrike von Luxburg¹, Matthias Rarey

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¹ Department of Computer Science, University of Tübingen , 72076 Tübingen, Germany.

DOI: 10.1021/acs.jcim.5b00332 PMID: 26740007

A common task in the hit-to-lead process is classifying sets of compounds into multiple, usually structural classes, which build the groundwork for subsequent SAR studies. Machine learning techniques can be used to automate this process by learning classification models from training compounds of each class. Gathering class information for compounds can be cost-intensive as the required data needs to be provided by human experts or experiments. This paper ... ...点击完成人机验证后继续浏览

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期刊名：Journal of chemical information and modeling

缩写：J CHEM INF MODEL

ISSN：1549-9596

e-ISSN：1549-960X

IF/分区：5.3/Q1

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