文章基本信息

标题：SimBoost : a read-across approach for predicting drug–target binding affinities using gradient boosting machines
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作者：Tong He ; Marten Heidemeyer ; Fuqiang Ban 等
期刊名称：Journal of Cheminformatics
印刷版ISSN：1758-2946
电子版ISSN：1758-2946
出版年度：2017
卷号：9
期号：1
页码：24
DOI：10.1186/s13321-017-0209-z
语种：English
出版社：BioMed Central
摘要：Computational prediction of the interaction between drugs and targets is a standing challenge in the field of drug discovery. A number of rather accurate predictions were reported for various binary drug–target benchmark datasets. However, a notable drawback of a binary representation of interaction data is that missing endpoints for non-interacting drug–target pairs are not differentiated from inactive cases, and that predicted levels of activity depend on pre-defined binarization thresholds. In this paper, we present a method called SimBoost that predicts continuous (non-binary) values of binding affinities of compounds and proteins and thus incorporates the whole interaction spectrum from true negative to true positive interactions. Additionally, we propose a version of the method called SimBoostQuant which computes a prediction interval in order to assess the confidence of the predicted affinity, thus defining the Applicability Domain metrics explicitly. We evaluate SimBoost and SimBoostQuant on two established drug–target interaction benchmark datasets and one new dataset that we propose to use as a benchmark for read-across cheminformatics applications. We demonstrate that our methods outperform the previously reported models across the studied datasets.
关键词：Read-across ; Gradient boosting ; Drug–target interaction ; Prediction interval ; Applicability Domain ; QSAR