Quantitative Nanostructure–Activity Relationships: Methods, Case Studies, and Perspectives

  • Denis FourchesEmail author
  • Ryan Lougee
Part of the Nanomedicine and Nanotoxicology book series (NANOMED)


In this chapter, we discuss the development and application of molecular modeling methods to analyze and forecast the experimental properties of nanomaterials. We mainly focus on Quantitative Nanostructure—Activity Relationships (QNAR) to evaluate the extent of biological activities potentially induced by various types of nanomaterials. First, we present the basic principles of QNAR modeling that uses machine-learning techniques to establish quantified links between the biological endpoint of interest (e.g., cytotoxicity, cell death, ROS production) and nanomaterials’ characteristics. Second, we briefly review recently published studies reporting on the QNAR modeling of the largest and most significant datasets of nanomaterials available in the public domain. Third, we discuss some perspectives for the use of molecular modeling on nanomaterials. Overall, we show how molecular modeling can represent a key element for enabling the rational design of nanomaterials with the desired activity and safety profile.


Molecular modeling Cheminformatics QNAR Machine learning Virtual screening 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.Department of Chemistry, Bioinformatics Research CenterNorth Carolina State UniversityRaleighUSA

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