Machine Learning Algorithm Identifies an Antibiotic Vocabulary for Permeating Gram-Negative Bacteria.,Journal of Chemical Information and Modeling

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Machine Learning Algorithm Identifies an Antibiotic Vocabulary for Permeating Gram-Negative Bacteria.
Journal of Chemical Information and Modeling ( IF 5.6 ) Pub Date : 2020-05-26 , DOI: 10.1021/acs.jcim.0c00352
Rachael A Mansbach ₁ , Inga V Leus ₂ , Jitender Mehla ₂ , Cesar A Lopez ₁ , John K Walker ₃ , Valentin V Rybenkov ₂ , Nicolas W Hengartner ₁ , Helen I Zgurskaya ₂ , S Gnanakaran ₁

Affiliation

Drug discovery faces a crisis. The industry has used up the “obvious” space in which to find novel drugs for biomedical applications, and productivity is declining. One strategy to combat this is rational approaches to expand the search space without relying on chemical intuition, to avoid rediscovery of similar spaces. In this work, we present proof of concept of an approach to rationally identify a “chemical vocabulary” related to a specific drug activity of interest without employing known rules. We focus on the pressing concern of multidrug resistance in Pseudomonas aeruginosa by searching for submolecules that promote compound entry into this bacterium. By synergizing theory, computation, and experiment, we validate our approach, explain the molecular mechanism behind identified fragments promoting compound entry, and select candidate compounds from an external library that display good permeation ability.

中文翻译：

机器学习算法可识别渗透革兰氏阴性细菌的抗生素词汇。

药物发现面临危机。该行业已经用尽了“明显”的空间，可以在其中找到用于生物医学应用的新药，并且生产率正在下降。解决这一问题的一种策略是在不依赖化学直觉的情况下扩展搜索空间的合理方法，以避免重新发现相似的空间。在这项工作中，我们提出了在不采用已知规则的情况下，合理地识别与所关注的特定药物活动有关的“化学词汇”的方法的概念证明。我们关注铜绿假单胞菌对多药耐药性的紧迫关注通过寻找促进化合物进入该细菌的亚分子。通过协同理论，计算和实验，我们验证了我们的方法，解释了识别的片段促进化合物进入的分子机制，并从具有良好渗透能力的外部文库中选择了候选化合物。

更新日期：2020-06-23

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