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Nanoinformatics: Predicting Toxicity Using Computational Modeling

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Computational Intelligence and Big Data Analytics

Part of the book series: SpringerBriefs in Applied Sciences and Technology ((BRIEFSFOMEBI))

Abstract

The various potential properties of nanomaterials make them prominent for most of the applications, used in our day-to-day life. Due to increase in the use of the nanomaterials, it is anticipated that the manufacturing and use of engineered nanoparticles (NP) will also grow rapidly. These engineered nanoparticles became toxic sometimes during the process of manufacturing as well as utilizing. Hence, there is a vital requirement for early recognition of toxic nature of these nanoparticles. Computational modelling is an effective approach for the same, which predicts toxicity on the basis of previous experimental data or molecular properties. QSAR is an emerging process to envision the toxicity based on their molecular structure properties. The various data mining techniques can accelerate this computation task and help in accurate and fast toxicity prediction. This paper leads the readers to work in this area, by providing a groundwork of the data, tools, and techniques used, along with future research directions.

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Authors and Affiliations

  1. Manipal University, Jaipur, India

    Bhavna Saini & Sumit Srivastava

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  1. Bhavna Saini
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  2. Sumit Srivastava
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Correspondence to Bhavna Saini .

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Saini, B., Srivastava, S. (2019). Nanoinformatics: Predicting Toxicity Using Computational Modeling. In: Computational Intelligence and Big Data Analytics. SpringerBriefs in Applied Sciences and Technology(). Springer, Singapore. https://doi.org/10.1007/978-981-13-0544-3_6

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