In silico screening of drug-membrane thermodynamics reveals linear relations between bulk partitioning and the potential of mean force.
In silico screening of drug-membrane thermodynamics reveals linear relations between bulk partitioning and the potential of mean force.
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DOI：
10.1063/1.4987012
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发表时间：
2017-06
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影响因子：
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通讯作者：
R. Menichetti;K. Kanekal;K. Kremer;T. Bereau
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文献类型：
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作者：
R. Menichetti;K. Kanekal;K. Kremer;T. Bereau
小分子在细胞膜中的分配是制药应用的一个关键参数，通常依赖于实验上可用的体积分配系数。计算机模拟提供了一个结构分辨率的插入热力学通过潜在的平均力，但需要显着的采样在原子水平。在这里，我们引入高通量粗粒度分子动力学模拟筛选热力学性质。这种基于物理学的模型在小分子的大规模研究中的应用建立了分配系数和平均力潜力的关键特征之间的线性关系。这使我们能够预测的结构，从批量实验测量超过40万化合物的插入。因此，平均力的潜力成为一个容易获得的量-已经因其对某些属性的高度可预测性而得到认可，例如，被动渗透此外，我们展示了粗粒化如何有助于减少化学空间的大小，从而实现筛选小分子的分层方法。
The partitioning of small molecules in cell membranes-a key parameter for pharmaceutical applications-typically relies on experimentally available bulk partitioning coefficients. Computer simulations provide a structural resolution of the insertion thermodynamics via the potential of mean force but require significant sampling at the atomistic level. Here, we introduce high-throughput coarse-grained molecular dynamics simulations to screen thermodynamic properties. This application of physics-based models in a large-scale study of small molecules establishes linear relationships between partitioning coefficients and key features of the potential of mean force. This allows us to predict the structure of the insertion from bulk experimental measurements for more than 400 000 compounds. The potential of mean force hereby becomes an easily accessible quantity-already recognized for its high predictability of certain properties, e.g., passive permeation. Further, we demonstrate how coarse graining helps reduce the size of chemical space, enabling a hierarchical approach to screening small molecules.