Diverse nanostructures of our in-house database
Nanoscience and technology have made a big impact on modern medicine, energy and other areas. However, experimental testing of nanomaterials for important physic-chemical properties, bioactivities and nanotoxicities is costly and time consuming. Informatics related studies are promising to answer this challenge by virtually designing new biocompatible nanomaterials. In Zhu lab, we want to create novel nanoinformatics tools for data curation, model developments and data/model sharing purposes. Novel nano-bio interaction models, including complex nanotoxicity models, were developed using modern machine learning approaches. This project will lead to the design of novel biocompatible nanomaterials with low toxicity and desired bioactivities.
Based on the annotated nanostructure, novel geometrical nanodescriptors were developed by employing Delaunay tessellation and atomic properties. The nanodescriptors digitalized nanomaterials by quantifying nanostructures. The nanodescriptors can be used to develop quantitative nanostructure activity relationship models.
ViNAS-Toolbox (Virtual Nanomaterial Simulation) is an open-source web portal aimed at collecting and annotating nanomaterials with virtual structure information and tested assay response information. ViNAS toolbox is broken into two complementary, overlapping databases. The ViNAS database contains 725 diverse nanomaterials and their metadata, such as classifications, shapes, and surface chemistry information. Additionally, simulated virtual nanostructures are available for download in as Protein DataBank (PDB) files. The second database ViNAS-Assay contains experimental data for the properties/activities/toxicities of these 725 nanomaterials.
Related Research Articles:
T. Wang, D.P. Russo, D. Bitounis, P. Demokritou, X. Jia, H. Huang, H. Zhu, Integrating structure annotation and machine learning approaches to develop graphene toxicity models, Carbon 204 (2023) 484-494.
D.P. Russo, X. Yan, S. Shende, H. Huang, B. Yan, H. Zhu, Virtual molecular projections and convolutional neural networks for the end-to-end modeling of nanoparticle activities and properties, Analytical Chemistry 92(20) (2020) 13971-13979.
X.L. Yan, A. Sedykh, W.Y. Wang, B. Yan, H. Zhu, Construction of a web-based nanomaterial database by big data curation and modeling friendly nanostructure annotations, Nat. Commun. 11 (1) (2020).
X.L. Yan, A. Sedykh, W.Y. Wang, X.L. Zhao, B. Yan, H. Zhu, In silico profiling nanoparticles: predictive nanomodeling using universal nanodescriptors and various machine learning approaches, Nanoscale 11 (17) (2019) 8352–8362.
W. Wang, A. Sedykh, H. Sun, L. Zhao, D.P. Russo, H. Zhou, B. Yan, H. Zhu, Predicting nano–bio interactions by integrating nanoparticle libraries and quantitative nanostructure activity relationship modeling, ACS Nano 11 (12) (2017) 12641–12649.