Pyxis Discovery, Delftechpark 26, 2628XH Delft, The Netherlands.
Department of Chemistry, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States.
J Med Chem. 2022 Aug 11;65(15):10300-10317. doi: 10.1021/acs.jmedchem.1c02090. Epub 2022 Jul 21.
Macrocyclic compounds (MCs) can have complex conformational properties that affect pharmacologically important behaviors such as membrane permeability. We measured the passive permeability of 3600 diverse nonpeptidic MCs and used machine learning to analyze the results. Incorporating selected properties based on the three-dimensional (3D) conformation gave models that predicted permeability with = 0.81. A biased spatial distribution of polar versus nonpolar regions was particularly important for good permeability, consistent with a mechanism in which the initial insertion of nonpolar portions of a MC helps facilitate the subsequent membrane entry of more polar parts. We also examined effects on permeability of 800 substructural elements by comparing matched molecular pairs. Some substitutions were invariably beneficial or invariably deleterious to permeability, while the influence of others was highly contextual. Overall, the work provides insights into how the permeability of MCs is influenced by their 3D conformational properties and suggests design hypotheses for achieving macrocycles with high membrane permeability.
大环化合物（MCs）可能具有复杂的构象特性，这些特性会影响膜通透性等药理学上重要的行为。我们测量了 3600 种不同的非肽类 MC 的被动通透性，并使用机器学习来分析结果。根据三维（3D）构象纳入选定的性质，可以得到预测通透性的模型， = 0.81。极性与非极性区域的偏置空间分布对于良好的通透性尤为重要，这与 MC 的非极性部分的初始插入有助于促进后续更极性部分进入膜的机制一致。我们还通过比较匹配的分子对来检查 800 个子结构元素对通透性的影响。一些取代对通透性始终有益或始终有害，而其他取代的影响则高度依赖于上下文。总的来说，这项工作深入了解了 MC 通透性如何受到其 3D 构象特性的影响，并为实现具有高膜通透性的大环化合物提供了设计假设。
