跨化学空间的药物膜渗透性,ACS Central Science

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跨化学空间的药物膜渗透性
ACS Central Science ( IF 12.7 ) Pub Date : 2019-01-08 00:00:00 , DOI: 10.1021/acscentsci.8b00718
Roberto Menichetti ₁ , Kiran H Kanekal ₁ , Tristan Bereau ₁

Affiliation

阐明小类药化合物的化学结构与其跨脂膜被动渗透速率之间的关系对于制药应用至关重要。不幸的是，用一些分子描述符表达的全面结构-渗透性关系的阐明受到了绝大多数可能化合物的阻碍。在这项工作中，我们先验地减少了化学空间的大小和多样性，以解决类似但平滑的结构-性质关系问题。这是通过依靠基于物理的粗粒度模型来实现的，该模型减小了化学空间的大小，从而能够在大大降低计算成本的情况下对该空间进行全面探索。我们执行高通量粗粒度 (HTCG) 模拟，根据两个简单的分子描述符（体分配自由能和 p K a）推导渗透率_表面。该表面是通过在高通量方案中详尽模拟代表小有机分子（范围从 30 到 160 Da）的所有粗粒化合物而构建的。我们提供酸性、碱性和两性离子化合物的结果。回到原子分辨率，HTCG 对超过 500 000 种化合物的预测使我们能够在特定化学基团和由此产生的渗透系数之间建立明确的联系，从而首次实现逆向设计程序。我们的结果对药物合成具有深远的影响：常用化学部分的优势缩小了渗透性范围。

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Drug–Membrane Permeability across Chemical Space

Unraveling the relation between the chemical structure of small druglike compounds and their rate of passive permeation across lipid membranes is of fundamental importance for pharmaceutical applications. The elucidation of a comprehensive structure–permeability relationship expressed in terms of a few molecular descriptors is unfortunately hampered by the overwhelming number of possible compounds. In this work, we reduce a priori the size and diversity of chemical space to solve an analogous—but smoothed out—structure–property relationship problem. This is achieved by relying on a physics-based coarse-grained model that reduces the size of chemical space, enabling a comprehensive exploration of this space with greatly reduced computational cost. We perform high-throughput coarse-grained (HTCG) simulations to derive a permeability surface in terms of two simple molecular descriptors—bulk partitioning free energy and pK_a. The surface is constructed by exhaustively simulating all coarse-grained compounds that are representative of small organic molecules (ranging from 30 to 160 Da) in a high-throughput scheme. We provide results for acidic, basic, and zwitterionic compounds. Connecting back to the atomic resolution, the HTCG predictions for more than 500 000 compounds allow us to establish a clear connection between specific chemical groups and the resulting permeability coefficient, enabling for the first time an inverse design procedure. Our results have profound implications for drug synthesis: the predominance of commonly employed chemical moieties narrows down the range of permeabilities.

更新日期：2019-01-08

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