Virtual nanostructure simulation (VINAS) portal

The use of nanomaterials, especially Engineered Nanomaterials (ENMs), in consumer products and medicine has been skyrocketing over the past decade. Various in vitro and in vivo studies evaluating the potential environmental and health effects of ENMs have generated vast quantities of experimental data, requiring urgent curation for information extraction, analysis/modeling, and data/model sharing using artificial intelligence methods. Computational modeling methods, especially machine learning and deep learning approaches, bear high expectations to develop predictive models for ENMs based on the available property/activity/toxicity data. Currently ENMs databases do not consist of nanostructure annotations to store diverse structural information in machine readable formats that are critical for computational modeling studies. To address this challenge in the current big data era, we will develop a large, publicly available ENMs portal that contains annotated nanostructures of more than 3,000 ENMs suitable for the computational modeling research, which will lead to the rational nanomedicine design. The ongoing Nanotechnology Health Implication Research (NHIR) consortium is providing high quality ENMs data for the initial ENMs database of this portal and will also support future data curations. This database will be designed based on Virtual Nanostructure Simulation (VINAS) technique, which will annotate the complex nanostructures into machine readable formats that are suitable for the machine learning modeling purpose. To this end, we will develop various new computational approaches to annotate the nanostructures, especially for complex ENMs (e.g. graphene derivatives). After that, we will use new machine learning and deep learning algorithms, such as additive model and explainable AI guided semi- supervised deep learning technique, to develop predictive models using the ENMs data of the curated database as the proof of concept. For example, a virtual nanomaterial projection approach that is based on deep learning, particularly the explainable AI guided semi-supervised generative adversarial networks, will be especially adept at handling the annotated nanostructures. In the VINAS database web portal as the final deliverables, the curated ENMs- bioactivity/property/toxicity data and annotated nanostructures will be shared as downloadable files for public community to use. And the resulting new deep learning predictive models will be shared as well. This study provides a new public platform to future data-driven nanoinformatics modeling studies, especially those machine learning based approaches, and can greatly advance the rational nanomedicine design and other areas of modern nanoinformatics.