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

机译：配体 - 靶相互作用综合预测的化学因子方法：比较研究

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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.
机译：化学MOMICS已成为跨学科领域，旨在最终以系统的方式识别所有靶家庭的所有可能的配体。越来越多的需要探索配体和目标的巨大空间最近引发了新型化学组织的新型计算技术的发展，这有可能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不断的关注。在这里，我们通过广泛评估多个靶家族的配体 - 靶相互作用的预测性能，揭示配体核和蛋白质核的不同组合如何影响性能以及如何在其方面变化目标家庭。

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