Chemogenomic approach to comprehensive predictions of ligand-target interactions: A comparative study

J. B. Brown, S. Niijima, A. Shiraishi, M. Nakatsui, Y. Okuno
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引用次数: 5

Abstract

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.
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综合预测配体-靶标相互作用的化学基因组学方法:一项比较研究
化学基因组学已经成为一个跨学科的领域,其目的是最终确定所有目标家族的所有可能的配体以系统的方式。近年来,对探索配体和靶标广阔空间的需求不断增加,这引发了化学基因组学新计算技术的发展,这些技术有可能在药物发现中发挥关键作用。其中,基于核的机器学习方法引起了越来越多的关注。本文通过广泛评估配体-靶标相互作用在5个靶标家族上的预测性能,探讨了几种配体-靶标核的适用性,揭示了配体核和蛋白核的不同组合对预测性能的影响,以及不同靶标家族的预测性能差异。
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