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主动搜索计算机辅助药物设计。

Active Search for Computer-aided Drug Design.

机构信息

School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham, NG8 1BB, United Kingdom.

Institut für Informatik III, Universität Bonn, Römerstraße 164, 53117, Bonn, Germany.

出版信息

Mol Inform. 2018 Jan;37(1-2). doi: 10.1002/minf.201700130. Epub 2018 Feb 1.

Abstract

We consider lead discovery as active search in a space of labelled graphs. In particular, we extend our recent data-driven adaptive Markov chain approach, and evaluate it on a focused drug design problem, where we search for an antagonist of an αv integrin, the target protein that belongs to a group of Arg-Gly-Asp integrin receptors. This group of integrin receptors is thought to play a key role in idiopathic pulmonary fibrosis, a chronic lung disease of significant pharmaceutical interest. As an in silico proxy of the binding affinity, we use a molecular docking score to an experimentally determined αvβ6 protein structure. The search is driven by a probabilistic surrogate of the activity of all molecules from that space. As the process evolves and the algorithm observes the activity scores of the previously designed molecules, the hypothesis of the activity is refined. The algorithm is guaranteed to converge in probability to the best hypothesis from an a priori specified hypothesis space. In our empirical evaluations, the approach achieves a large structural variety of designed molecular structures for which the docking score is better than the desired threshold. Some novel molecules, suggested to be active by the surrogate model, provoke a significant interest from the perspective of medicinal chemistry and warrant prioritization for synthesis. Moreover, the approach discovered 19 out of the 24 active compounds which are known to be active from previous biological assays.

摘要

我们将药物先导物的发现视为在标记图空间中的主动搜索。具体来说,我们扩展了最近的数据驱动自适应马尔可夫链方法,并将其应用于一个有针对性的药物设计问题上,该问题涉及寻找一种αv 整合素拮抗剂,即目标蛋白属于 Arg-Gly-Asp 整合素受体群。该整合素受体群被认为在特发性肺纤维化(一种具有重要药物研发价值的慢性肺部疾病)中起着关键作用。作为结合亲和力的计算代理,我们使用分子对接评分来评估与实验确定的 αvβ6 蛋白结构的亲和力。搜索是由来自该空间的所有分子的活性概率代理驱动的。随着过程的发展和算法观察到先前设计分子的活性评分,对活性的假设得到了改进。该算法保证可以从先验指定的假设空间中以概率收敛到最佳假设。在我们的实证评估中,该方法实现了设计分子结构的较大结构多样性,其对接评分优于所需的阈值。一些新型分子,根据替代模型被认为具有活性,从药物化学的角度来看引起了极大的兴趣,并值得优先考虑合成。此外,该方法发现了已知在先前的生物测定中具有活性的 24 种活性化合物中的 19 种。

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