Wang Wenyi, Yan Xiliang, Zhao Linlin, Russo Daniel P, Wang Shenqing, Liu Yin, Sedykh Alexander, Zhao Xiaoli, Yan Bing, Zhu Hao
The Rutgers Center for Computational and Integrative Biology, Camden, NJ, 08102, USA.
School of Chemistry and Chemical Engineering, Shandong University, Jinan, 250100, China.
J Cheminform. 2019 Jan 18;11(1):6. doi: 10.1186/s13321-019-0329-8.
To facilitate the development of new nanomaterials, especially nanomedicines, a novel computational approach was developed to precisely predict the hydrophobicity of gold nanoparticles (GNPs). The core of this study was to develop a large virtual gold nanoparticle (vGNP) library with computational nanostructure simulations. Based on the vGNP library, a nanohydrophobicity model was developed and then validated against externally synthesized and tested GNPs. This approach and resulted model is an efficient and effective universal tool to visualize and predict critical physicochemical properties of new nanomaterials before synthesis, guiding nanomaterial design.
为促进新型纳米材料尤其是纳米药物的开发,人们开发了一种新颖的计算方法来精确预测金纳米颗粒(GNP)的疏水性。本研究的核心是通过计算纳米结构模拟来构建一个大型虚拟金纳米颗粒(vGNP)库。基于该vGNP库,开发了一种纳米疏水性模型,然后针对外部合成和测试的GNP进行了验证。这种方法及所得模型是一种高效且有效的通用工具,可在合成前可视化和预测新型纳米材料的关键物理化学性质,从而指导纳米材料的设计。