An Online Nanoinformatics Platform Empowering Computational Modeling of Nanomaterials by Nanostructure Annotations and Machine Learning Toolkits
Modern nanotechnology has generated numerous datasets from <i>in vitro</i> and <i>in vivo</i> 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
- Daniel P. Russo (ORCID: https://orcid.org/0000-0003-0438-1667)
- Tong Wang (ORCID: https://orcid.org/0000-0002-6719-7368)
- Hao Zhu (ORCID: https://orcid.org/0000-0002-3559-6129)
- Philip Demokritou (ORCID: https://orcid.org/0000-0002-2484-9143)
- Heng Huang (ORCID: https://orcid.org/0000-0002-3483-8333)
- Xuelian Jia (ORCID: https://orcid.org/0000-0001-9901-9104)
- Xinyu Yang (ORCID: https://orcid.org/0000-0002-4124-7144)
Institutions
- Rutgers, The State University of New Jersey (US)
- Tulane University (US)
- Harvard University (US)
- Environmental and Occupational Health Sciences Institute (US)
- Rowan University (US)
- University of Maryland, College Park (US)
Publication Details
- Journal
- Nano Letters
- Published
- 2024-08-09
- DOI
- https://doi.org/10.1021/acs.nanolett.4c02568
- Citations
- 15
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 1.76
Funders
- National Institute of General Medical Sciences
- National Institute of Environmental Health Sciences
- National Institute of Child Health and Human Development
- Division of Information and Intelligent Systems