Abstract

Nowadays, the impact of engineered nanoparticles (NPs) on human health and environment has aroused widespread attention. It is essential to assess and predict the biological activity, toxicity, and physicochemical properties of NPs. Computation-based methods have been developed to be efficient alternatives for understanding the negative effects of nanoparticles on the environment and human health. Here, a classification-based structure-activity relationship model for nanoparticles (nano-SAR) was developed to predict the cellular uptake of 109 functionalized magneto-fluorescent nanoparticles to pancreatic cancer cells (PaCa2). The norm index descriptors were employed for describing the structure characteristics of the involved nanoparticles. The Random forest algorithm (RF), combining with the Recursive Feature Elimination (RFE) was employed to develop the nano-SAR model. The resulted model showed satisfactory statistical performance, with the accuracy (ACC) of the test set and the training set of 0.950 and 0.966, respectively, demonstrating that the model had satisfactory classification effect. The model was rigorously verified and further extensively compared with models in the literature. The proposed model could be reasonably expected to predict the cellular uptakes of nanoparticles and provide some guidance for the design and manufacture of safer nanomaterials.

Highlights

  • IntroductionNanotechnology has been considered as one of the key enabling technologies for global economic growth

  • More and more attention has been paid to the inherent disadvantages of nanomaterials and the resulting hazards that may be exposed in the workplace among consumers and in the environment

  • True positive represents that a toxic metal oxide nanoparticles (MNPs) is correctly classified as positive, true negative (TN)

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Summary

Introduction

Nanotechnology has been considered as one of the key enabling technologies for global economic growth. With the continuous development of nanotechnology, new kinds of nanomaterials are springing up all over the world [1,2,3]. Nanomaterials are widely used in traditional materials, catalysis [4], medical devices [5,6], electronic equipment [7], coatings, and other industries [8,9,10] owing to their unique properties, such as excellent optical, electrical, and magnetic properties. More and more attention has been paid to the inherent disadvantages of nanomaterials and the resulting hazards that may be exposed in the workplace among consumers and in the environment. Recent studies have found that some nanomaterials may have biological hazards, understanding of the adverse effects of these products is still in its infancy

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