Similarity Boosted Quantitative Structure鈥揂ctivity Relationship鈥擜 Systematic Study of Enhancing Structural Descriptors by Molecular Similarity

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• 作者：Tobias Girschick ; Pedro R. Almeida ; Stefan Kramer ; Jonna St氓lring
• 刊名：Journal of Chemical Information and Modeling
• 出版年：2013
• 出版时间：May 24, 2013
• 年：2013
• 卷：53
• 期：5
• 页码：1017-1025
• 全文大小：432K
• 年卷期：v.53,no.5(May 24, 2013)
• ISSN：1549-960X

文摘

The concept of molecular similarity is one of the most central in the fields of predictive toxicology and quantitative structure鈥揳ctivity relationship (QSAR) research. Many toxicological responses result from a multimechanistic process and, consequently, structural diversity among the active compounds is likely. Combining this knowledge, we introduce similarity boosted QSAR modeling, where we calculate molecular descriptors using similarities with respect to representative reference compounds to aid a statistical learning algorithm in distinguishing between different structural classes. We present three approaches for the selection of reference compounds, one by literature search and two by clustering. Our experimental evaluation on seven publicly available data sets shows that the similarity descriptors used on their own perform quite well compared to structural descriptors. We show that the combination of similarity and structural descriptors enhances the performance and that a simple stacking approach is able to use the complementary information encoded by the different descriptor sets to further improve predictive results. All software necessary for our experiments is available within the cheminformatics software framework AZOrange.