In silico profiling nanoparticles: predictive nanomodeling using universal nanodescriptors and various machine learning approaches-Reference-Cited by-同舟云学术

In silico profiling nanoparticles: predictive nanomodeling using universal nanodescriptors and various machine learning approaches

Published:2019 Issue:17 Volume:11 Page:8352-8362
ISSN:2040-3364
Container-title:Nanoscale
language:en
Short-container-title:Nanoscale

Author:

Yan Xiliang¹²³⁴⁵^ORCID,Sedykh Alexander⁵⁶⁷⁸⁹,Wang Wenyi⁵⁶⁷,Zhao Xiaoli¹⁰¹¹¹²⁷,Yan Bing¹³¹⁴¹⁵¹⁶¹⁷^ORCID,Zhu Hao⁵⁶⁷¹⁸¹⁹^ORCID

Affiliation:

1. School of Chemistry and Chemical Engineering

2. Shandong University

3. Jinan 250100

4. China

5. The Rutgers Center for Computational and Integrative Biology

6. Camden

7. USA

8. Sciome

9. Research Triangle Park

10. Department of Physiological Science

11. Eastern Virginia Medical School

12. Norfolk

13. Key Laboratory for Water Quality and Conservation of the Pearl River Delta

14. Ministry of Education

15. Institute of Environmental Research at Greater Bay

16. Guangzhou University

17. Guangzhou 510006

18. Department of Chemistry

19. Rutgers University

Abstract

We designed novel nanodescriptors that can characterize the nanostructure diversity and also be quickly calculated in batches, to profile nanoparticles.

Funder

National Natural Science Foundation of China

Chinese Academy of Sciences

Publisher

Royal Society of Chemistry (RSC)

Subject

General Materials Science

Link

http://pubs.rsc.org/en/content/articlepdf/2019/NR/C9NR00844F

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