Practical Outcomes of Applying Ensemble Machine Learning Classifiers to High-Throughput Screening (HTS) Data Analysis and Screening

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• 作者：Kirk Simmons ; John Kinney ; Aaron Owens ; Daniel A. Kleier ; Karen Bloch ; Dave Argentar ; Alicia Walsh ; Ganesh Vaidyanathan
• 刊名：Journal of Chemical Information and Modeling
• 出版年：2008
• 出版时间：November 24, 2008
• 年：2008
• 卷：48
• 期：11
• 页码：2196-2206
• 全文大小：339K
• 年卷期：v.48,no.11(November 24, 2008)
• ISSN：1549-960X

文摘

Over the years numerous papers have presented the effectiveness of various machine learning methods in analyzing drug discovery biological screening data. The predictive performance of models developed using these methods has traditionally been evaluated by assessing performance of the developed models against a portion of the data randomly selected for holdout. It has been our experience that such assessments, while widely practiced, result in an optimistic assessment. This paper describes the development of a series of ensemble-based decision tree models, shares our experience at various stages in the model development process, and presents the impact of such models when they are applied to vendor offerings and the forecasted compounds are acquired and screened in the relevant assays. We have seen that well developed models can significantly increase the hit-rates observed in HTS campaigns.