Experimental modulation and computational model of nano-hydrophobicity

Li, Shuhuan; Zhai, Shumei; Liu, Yin; Zhou, Hongyu; Wu, Jinmei; Jiao, Qing; Zhang, Bin; Zhu, Hao; Yan, Bing

doi:10.1016/j.biomaterials.2015.02.043

Cited by 41 publications

(56 citation statements)

References 31 publications

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“…In contrast, when the gold nanoparticles were synthesized to have gradual increase in hydrophobicity, cell uptake and cytotoxicity increased with increasing experimental LogP values (an experimental surrogate for the lipophilicity or polarity of a material) (Fig. 3b) (Li et al, 2015). …”

Section: Systematic Chemical and Physicochemical Modificationsmentioning

confidence: 99%

“…Moreover, due to the importance and complexity of the surface microenvironment, new types of nano-descriptors are urgently needed to be developed. For example, Li et al recently simulated the accessibility of water molecules near functionalized nanoparticle surface ligands by modelling nano-hydrophobicity (Li et al, 2015). In the study, the traditional chemical descriptors were weighed by the accessibility of water molecules to fit the condition of nanostructures.…”

Section: Nano-bio Interaction Data Nanoinformatics and Modellingmentioning

confidence: 99%

“…Recent published work has highlighted the potential for machine learning models to facilitate purposeful design of nanomaterials with bespoke properties, particularly for medical applications or to minimize adverse biological effects. (Fourches et al, 2015; Le et al, 2015; Li et al, 2015; Liu et al, 2015b; Zhang et al, 2016). Fourches et al, for example, used a computational model to virtually screen external libraries and design new carbon nanotubes with favoured properties.…”

Section: Nano-bio Interaction Data Nanoinformatics and Modellingmentioning

confidence: 99%

“…(b) Cell uptake of GNPs (25 mg mL −1 ) with a change in experimental LogP in (PMA)-induced THP-1 macrophages after a 24 h incubation. Adapted with permission from reference (Su et al, 2012), copyright 2012 American Chemical Society, and reference (Li et al, 2015), copyright 2015 Elsevier Ltd.…”

Section: Figmentioning

confidence: 99%

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Toward a systematic exploration of nano-bio interactions

Bai

Liu

et al. 2017

Toxicology and Applied Pharmacology

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Many studies of nanomaterials make non-systematic alterations of nanoparticle physicochemical properties. Given the immense size of the property space for nanomaterials, such approaches are not very useful in elucidating fundamental relationships between inherent physicochemical properties of these materials and their interactions with, and effects on, biological systems. Data driven artificial intelligence methods such as machine learning algorithms have proven highly effective in generating models with good predictivity and some degree of interpretability. They can provide a viable method of reducing or eliminating animal testing. However, careful experimental design with the modelling of the results in mind is a proven and efficient way of exploring large materials spaces. This approach, coupled with high speed automated experimental synthesis and characterization technologies now appearing, is the fastest route to developing models that regulatory bodies may find useful. We advocate greatly increased focus on systematic modification of physicochemical properties of nanoparticles combined with comprehensive biological evaluation and computational analysis. This is essential to obtain better mechanistic understanding of nano-bio interactions, and to derive quantitatively predictive and robust models for the properties of nanomaterials that have useful domains of applicability.

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Section: Systematic Chemical and Physicochemical Modificationsmentioning

confidence: 99%

Section: Nano-bio Interaction Data Nanoinformatics and Modellingmentioning

confidence: 99%

Section: Nano-bio Interaction Data Nanoinformatics and Modellingmentioning

confidence: 99%

Section: Figmentioning

confidence: 99%

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Toward a systematic exploration of nano-bio interactions

Bai

Liu

et al. 2017

Toxicology and Applied Pharmacology

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“…Although the descriptors calculated only from the surface ligands are useful in predicting certain properties of nanoparticles, 14–16 the effects of the nanoparticle size, density, position, distribution, length, and type of surface ligands on the biological properties were not considered in these studies. Some other studies have incorporated descriptors derived from some nanoparticle-related properties ( e.g ., nanoparticle size) 17–20 or testing results ( e.g ., proteomics data) 6,21–23 for computational models. Efforts were also made to combine molecular simulations and QSAR modeling.…”

mentioning

confidence: 99%

Predicting Nano–Bio Interactions by Integrating Nanoparticle Libraries and Quantitative Nanostructure Activity Relationship Modeling

et al. 2017

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The discovery of biocompatible or bioactive nanoparticles for medicinal applications is an expensive and time-consuming process that may be significantly facilitated by incorporating more rational approaches combining both experimental and computational methods. However, it is currently hindered by two limitations: (1) the lack of high-quality comprehensive data for computational modeling and (2) the lack of an effective modeling method for the complex nanomaterial structures. In this study, we tackled both issues by first synthesizing a large library of nanoparticles and obtained comprehensive data on their characterizations and bioactivities. Meanwhile, we virtually simulated each individual nanoparticle in this library by calculating their nanostructural characteristics and built models that correlate their nanostructure diversity to the corresponding biological activities. The resulting models were then used to predict and design nanoparticles with desired bioactivities. The experimental testing results of the designed nanoparticles were consistent with the model predictions. These findings demonstrate that rational design approaches combining high-quality nanoparticle libraries, big experimental data sets, and intelligent computational models can significantly reduce the efforts and costs of nanomaterial discovery.

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A New Strategy for Microbial Taxonomic Identification through Micro‐Biosynthetic Gold Nanoparticles and Machine Learning

et al. 2022

Advanced Materials

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Scheme 1. Schematic illustration of microbial taxonomic identification through biosynthetic AuNPs and machine learning. 1) Microorganism-mediated biosynthesis of AuNPs in solution. 2) Characterizations of biosynthetic AuNPs including diameter, SPR spectrum, surface potential, and transmission electron microscopy (TEM). 3) Features of the biosynthetic AuNPs as a data set of input for machine-learning-based analysis. 4) Data processing and analysis including PCA, LDA, and RF. 5) Output of the data analysis for visualization, including scatter charts, histograms, and line charts. 6) Taxonomic identification and the clustering tree through machine-learning-based analysis of biosynthetic AuNPs.

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Experimental modulation and computational model of nano-hydrophobicity

Cited by 41 publications

References 31 publications

Toward a systematic exploration of nano-bio interactions

Toward a systematic exploration of nano-bio interactions

Predicting Nano–Bio Interactions by Integrating Nanoparticle Libraries and Quantitative Nanostructure Activity Relationship Modeling

A New Strategy for Microbial Taxonomic Identification through Micro‐Biosynthetic Gold Nanoparticles and Machine Learning

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