Chemogenomic approach to comprehensive predictions of ligand-target interactions: A comparative study
Chemogenomic approach to comprehensive predictions of ligand-target interactions: A comparative study
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DOI：
10.1109/bibmw.2012.6470295
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发表时间：
2012-10
期刊：
影响因子：
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通讯作者：
J. B. Brown;S. Niijima;A. Shiraishi;M. Nakatsui;Y. Okuno
中科院分区：
文献类型：
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作者：
J. B. Brown;S. Niijima;A. Shiraishi;M. Nakatsui;Y. Okuno
化学基因组学已经成为一个跨学科的领域，其目的是以系统的方式最终确定所有靶家族的所有可能配体。近年来，随着对配体和靶点的研究需求的不断增长，化学基因组学计算技术的发展也受到了人们的关注，这将在药物发现中发挥至关重要的作用。其中，基于内核的机器学习方法引起了越来越多的关注。在这里，我们通过广泛评估配体-靶相互作用对五个靶家族的预测性能来探索几种配体-靶核的适用性，并揭示配体核和蛋白质核的不同组合如何影响性能以及性能如何在靶家族之间变化。
Chemogenomics has emerged as an interdisciplinary field that aims to ultimately identify all possible ligands of all target families in a systematic manner. An ever-increasing need to explore the vast space of both ligands and targets has recently triggered the development of novel computational techniques for chemogenomics, which have the potential to play a crucial role in drug discovery. Among others, a kernel-based machine learning approach has attracted increasing attention. Here, we explore the applicability of several ligand-target kernels by extensively evaluating the prediction performance of ligand-target interactions on five target families, and reveal how different combinations of ligand kernels and protein kernels affect the performance and also how the performance varies between the target families.