Quantitative Structure–Property Relationship Modeling of Diverse Materials Properties

Le, Tu C.; Epa, V. Chandana; Burden, Frank R.; Winkler, David A.

doi:10.1021/cr200066h

Cited by 476 publications

(397 citation statements)

References 196 publications

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“…To this end, (Le, Epa, Burden, & Winkler, 2012) suggested to combine molecular descriptors characterising physicochemical properties of compounds with historical descriptors describing the sample preparation and synthesis techniques of materials in order to develop reliable and predictive models. Although historical descriptors can be useful for modelling traditional materials, their implementation to nano-(Q)SAR models can be very difficult since they would probably have no ability to distinguish between ordinary and nano-sized particles.…”

Section: Np-specific Descriptorsmentioning

confidence: 99%

(Q)SAR modelling of nanomaterial toxicity: A critical review

et al. 2015

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There is an increasing recognition that nanomaterials pose a risk to human health, and that the novel engineered nanomaterials (ENMs) in the nanotechnology industry and their increasing industrial usage poses the most immediate problem for hazard assessment, as many of them remain untested. The large number of materials and their variants (different sizes and coatings for instance) that require testing and ethical pressure towards non-animal testing means that expensive animal bioassay is precluded, and the use of (quantitative) structure activity relationships ((Q)SAR) models as an alternative source of hazard information should be explored.(Q)SAR modelling can be applied to fill the critical knowledge gaps by making the best use of existing data, prioritize physicochemical parameters driving toxicity, and provide practical solutions to the risk assessment problems caused by the diversity of ENMs. This paper covers the core components required for successful application of (Q)SAR technologies to ENMs toxicity prediction, and summarizes the published nano-(Q)SAR studies and outlines the challenges ahead for nano-(Q)SAR modelling. It provides a critical review of (1) the present status of the availability of ENMs characterization/toxicity data, (2) the characterization of nanostructures that meets the need of (Q)SAR analysis, (3) the summary of published nano-(Q)SAR studies and their limitations, (4) the in silico tools for (Q)SAR screening of nanotoxicity and (5) the prospective directions for the development of nano-(Q)SAR models.

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Section: Np-specific Descriptorsmentioning

confidence: 99%

(Q)SAR modelling of nanomaterial toxicity: A critical review

et al. 2015

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“…57 We contend, however, that it remains to be determined whether the real challenges in oxide-based electronics are of the sort suitable for only data-driven scientific discovery approaches. For instance, the subject of m1-different configurations of artificially grown structures-cannot be captured even in principle by any database because of the limitless configuration space.…”

Section: -3mentioning

confidence: 99%

Research Update: Towards designed functionalities in oxide-based electronic materials

2015

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“…[3][4][5][6][7][8][9] Materials discovery is now increasingly been carried out using high throughput synthesis and characterization methods so that novel, useful areas of materials property space can be identified. [10][11][12][13][14][15][16][17][18][19] While high throughput experimental techniques can dramatically accelerate new materials discovery, it is essential that complementary computational and informatics techniques are also are also be used. These tools allow the efficient extraction of useful information from the large data sets generated by high throughput materials experiments.…”

Section: Introductionmentioning

confidence: 99%

“…[14,[22][23][24] Here we show how these modelling techniques can be applied to high throughput bacterial adhesion data to successfully and quantitatively predict pathogen attachment to the surfaces of polymers in a large polymer library. These methods will accelerate discovery and optimization of materials with very low pathogen attachment that can be used to construct the next generation of medical devices to reduce nosocomial infections.…”

Section: Introductionmentioning

confidence: 99%

Modelling and Prediction of Bacterial Attachment to Polymers

Epa

Hook

Chang

et al. 2013

Adv Funct Materials

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Infection by pathogenic bacteria on implanted and indwelling medical devices during surgery causes large morbidity and mortality worldwide. Attempts to ameliorate this important medical issue have included development of antimicrobial surfaces on materials, 'no touch' surgical procedures, and development of materials with inherent low pathogen attachment.The search for new materials is increasingly being carried out by high throughput methods.Efficient methods for extracting knowledge from these large data sets are essential. We used data from a large polymer microarray exposed to three clinical pathogens to derive robust and predictive machine-learning models of pathogen attachment. The models could predict pathogen attachment for the polymer library quantitatively. The models also successfully 22222222224222 predicted pathogen attachment for a second-generation library, and identified polymer surface chemistries that enhance or diminish pathogen attachment.

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Quantitative Structure–Property Relationship Modeling of Diverse Materials Properties

Abstract: Possible terms to include :Term (definition) Alternative or related terms 3D QSAR (three-dimensional quantitative structureactivity relationships)Comparative molecular field analysis (CoMFA), Comparative molecular similarity index analysis (COMSIA), molecular field analysis.

Cited by 476 publications

References 196 publications

(Q)SAR modelling of nanomaterial toxicity: A critical review

(Q)SAR modelling of nanomaterial toxicity: A critical review

Research Update: Towards designed functionalities in oxide-based electronic materials

Modelling and Prediction of Bacterial Attachment to Polymers

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