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Exploring the Chemical Space of Urease Inhibitors to Extract Meaningful Trends and Drivers of Activity
Journal of Chemical Information and Modeling ( IF 5.6 ) Pub Date : 2022-06-06 , DOI: 10.1021/acs.jcim.2c00150
Natália Aniceto 1, 2 , Vasco D B Bonifácio 3, 4, 5 , Rita C Guedes 1, 2 , Nuno Martinho 3, 4
Affiliation  

Blocking the catalytic activity of urease has been shown to have a key role in different diseases as well as in different agricultural applications. A vast array of molecules have been tested against ureases of different species, but the clinical translation of these compounds has been limited due to challenges of potency, chemical and metabolic stability as well as promiscuity against other proteins. The design and development of new compounds greatly benefit from insights from previously tested compounds; however, no large-scale studies surveying the urease inhibitors’ chemical space exist that can provide an overview of developed compounds to data. Therefore, given the increasing interest in developing new compounds for this target, we carried out a comprehensive analysis of the activity landscape published so far. To do so, we assembled and curated a data set of compounds tested against urease. To the best of our knowledge, this is the largest data set of urease inhibitors to date, composed of 3200 compounds of diverse structures. We characterized the data set in terms of chemical space coverage, molecular scaffolds, distribution with respect to physicochemical properties, as well as temporal trends of drug development. Through these analyses, we highlighted different substructures and functional groups responsible for distinct activity and inactivity against ureases. Furthermore, activity cliffs were assessed, and the chemical space of urease inhibitors was compared to DrugBank. Finally, we extracted meaningful patterns associated with activity using a decision tree algorithm. Overall, this study provides a critical overview of urease inhibitor research carried out in the last few decades and enabled finding underlying SAR patterns such as under-reported chemical functional groups that contribute to the overall activity. With this work, we propose different rules and practical implications that can guide the design or selection of novel compounds to be screened as well as lead optimization.

中文翻译:

探索脲酶抑制剂的化学空间以提取有意义的趋势和活性驱动因素

阻断脲酶的催化活性已被证明在不同的疾病以及不同的农业应用中具有关键作用。已经针对不同物种的脲酶测试了大量分子,但由于效力、化学和代谢稳定性以及与其他蛋白质的混杂性等挑战,这些化合物的临床转化受到限制。新化合物的设计和开发极大地受益于先前测试化合物的见解;然而,目前还没有调查脲酶抑制剂化学空间的大规模研究可以提供已开发化合物的概览数据。因此,鉴于人们对为此目标开发新化合物的兴趣越来越大,我们对迄今为止发表的活动概况进行了全面分析。为此,我们收集并整理了一组针对脲酶进行测试的化合物数据集。据我们所知,这是迄今为止最大的脲酶抑制剂数据集,由 3200 种不同结构的化合物组成。我们根据化学空间覆盖率、分子支架、物理化学特性的分布以及药物开发的时间趋势对数据集进行了表征。通过这些分析,我们强调了不同的子结构和功能组负责对脲酶的不同活动和不活动。此外,还评估了活性悬崖,并将脲酶抑制剂的化学空间与 DrugBank 进行了比较。最后,我们使用决策树算法提取了与活动相关的有意义的模式。全面的,这项研究提供了对过去几十年进行的脲酶抑制剂研究的重要概述,并能够找到潜在的 SAR 模式,例如对整体活性有贡献的未报告的化学官能团。通过这项工作,我们提出了不同的规则和实际意义,可以指导设计或选择要筛选的新型化合物以及先导化合物优化。
更新日期:2022-06-06
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