Yamanishi Yoshihiro
Institut Curie, Centre de recherche Biologie du developpement, U900 Unit of Bioinformatics and Computational Systems Biology of Cancer, Paris, France.
Methods Mol Biol. 2013;939:97-113. doi: 10.1007/978-1-62703-107-3_9.
The identification of drug-target interactions from heterogeneous biological data is critical in the drug development. In this chapter, we review recently developed in silico chemogenomic approaches to infer unknown drug-target interactions from chemical information of drugs and genomic information of target proteins. We review several kernel-based statistical methods from two different viewpoints: binary classification and dimension reduction. In the results, we demonstrate the usefulness of the methods on the prediction of drug-target interactions from chemical structure data and genomic sequence data. We also discuss the characteristics of each method, and show some perspectives toward future research direction.
从异质生物数据中识别药物-靶点相互作用在药物开发中至关重要。在本章中，我们回顾了最近开发的计算机化学基因组学方法，这些方法可根据药物的化学信息和靶蛋白的基因组信息推断未知的药物-靶点相互作用。我们从两个不同的角度回顾了几种基于核的统计方法：二元分类和降维。结果表明，这些方法在从化学结构数据和基因组序列数据预测药物-靶点相互作用方面是有用的。我们还讨论了每种方法的特点，并展示了对未来研究方向的一些展望。
