An Online Nanoinformatics Platform Empowering Computational Modeling of Nanomaterials by Nanostructure Annotations and Machine Learning Toolkits.

Journal: Nano letters
PMID: 39120132

Abstract

Modern nanotechnology has generated numerous datasets from and studies on nanomaterials, with some available on nanoinformatics portals. However, these existing databases lack the digital data and tools suitable for machine learning studies. Here, we report a nanoinformatics platform that accurately annotates nanostructures into machine-readable data files and provides modeling toolkits. This platform, accessible to the public at https://vinas-toolbox.com/, has annotated nanostructures of 14 material types. The associated nanodescriptor data and assay test results are appropriate for modeling purposes. The modeling toolkits enable data standardization, data visualization, and machine learning model development to predict properties and bioactivities of new nanomaterials. Moreover, a library of virtual nanostructures with their predicted properties and bioactivities is available, directing the synthesis of new nanomaterials. This platform provides a data-driven computational modeling platform for the nanoscience community, significantly aiding in the development of safe and effective nanomaterials.

Authors

  • Tong Wang
    School of Public Health, Shanxi Medical University, Taiyuan 030000, China; Key Laboratory of Coal Environmental Pathogenicity and Prevention (Shanxi Medical University), Ministry of Education, Taiyuan 030000, China.
  • Daniel P Russo
    Collaborations Pharmaceuticals, Inc. , 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States.
  • Philip Demokritou
    Center for Nanotechnology and Nanotoxicology, HSPH-NIEHS Nanosafety Center, Department of Environmental Health, Harvard T.H. Chan School of Public Health, Harvard University, 665 Huntington, Boston, MA 02115, USA.
  • Xuelian Jia
    Department of Chemistry and Biochemistry, Rowan University, Glassboro, New Jersey 08028, United States.
  • Heng Huang
    Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, USA.
  • Xinyu Yang
    Department of Gastroenterology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China.
  • Hao Zhu
    State Key Laboratory of Advanced Technology for Materials Synthesis and Processing, Wuhan University of Technology Wuhan 430070 PR China chang@whut.edu.cn suntl@whut.edu.cn.

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