计算机化学基因组学的核心方法
Kernel methods for in silico chemogenomics
摘要：
预测小分子和蛋白质之间的相互作用是药物发现过程的关键组成部分。特别是，准确的预测模型越来越多地用于从大分子数据库中预选潜在的先导化合物，或筛选副作用。虽然经典的计算机模拟方法专注于预测与给定特定靶标的相互作用，但新的化学基因组学方法采用跨靶标观点。基于最近在生物和化学信息学中使用核方法的发展，我们提出了一个系统的框架来筛选小分子的化学空间与蛋白质的生物空间的相互作用。我们表明，这个框架允许跨目标的信息共享，从而大大提高了三个重要类别的药物靶点的配体预测准确性：酶，GPCR和离子通道。
英文摘要：
Predicting interactions between small molecules and proteins is a crucial ingredient of the drug discovery process. In particular, accurate predictive models are increasingly used to preselect potential lead compounds from large molecule databases, or to screen for side-effects. While classical in silico approaches focus on predicting interactions with a given specific target, new chemogenomics approaches adopt cross-target views. Building on recent developments in the use of kernel methods in bio- and chemoinformatics, we present a systematic framework to screen the chemical space of small molecules for interaction with the biological space of proteins. We show that this framework allows information sharing across the targets, resulting in a dramatic improvement of ligand prediction accuracy for three important classes of drug targets: enzymes, GPCR and ion channels.
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