Dataset Information

Universal nanohydrophobicity predictions using virtual nanoparticle library.

ABSTRACT: 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.

SUBMITTER: Wang W

PROVIDER: S-EPMC6689884 | biostudies-other | 2019 Jan

REPOSITORIES: biostudies-other

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Universal nanohydrophobicity predictions using virtual nanoparticle library.

Wang Wenyi W Yan Xiliang X Zhao Linlin L Russo Daniel P DP Wang Shenqing S Liu Yin Y Sedykh Alexander A Zhao Xiaoli X Yan Bing B Zhu Hao H

Journal of cheminformatics 20190118 1

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 ...[more]

PMID: 30659400

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Dataset Information

Universal nanohydrophobicity predictions using virtual nanoparticle library.

Publications

Universal nanohydrophobicity predictions using virtual nanoparticle library.

Similar Datasets

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