Virtual nanostructure simulation (VINAS) portal

虚拟纳米结构模拟 (VINAS) 门户

基本信息

批准号：
10567076
负责人：
Hao Zhu
金额：
$ 16.89万
依托单位：
ROWAN UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-05-01 至 2023-10-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10567076
关键词：
Acceleration Address Animal Model Area Big Data Biological Assay Buffers Carbon Nanotubes Cells Chemical Structure Chemistry Communities Complex Computer Models Cyclic Peptides DNA Data Data Collection Databases Dendrimers Development Ensure Fullerenes Future Health Health Technology In Vitro Inflammatory Response Information Retrieval Lead Ligands Machine Learning Manuals Manuscripts Medicine Methods Modeling Modernization Names Nanostructures Nanotechnology Oxidative Stress Induction Peptide Nanotubes Physics Procedures Property Proteins Protocols documentation PubChem Quantum Dots Readability Research Resources Structure Study models Surface Techniques Temperature Testing Toxic effect artificial intelligence method chemical property consumer product cytotoxicity data curation data repository data sharing deep learning deep learning algorithm deep learning model design expectation experimental study generative adversarial network graphene in vivo large datasets learning strategy machine learning model nano nanoengineering nanomaterials nanomedicine nanoparticle novel predictive modeling programs protein data bank public database response simulation small molecule success tool uptake user-friendly virtual web portal zeta potential

项目摘要

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.

纳米材料，特别是工程纳米材料（ENM）在消费领域的应用在过去的十年里，产品和药品一直在飞速增长。各种体外和体内评估 ENM 的潜在环境和健康影响的研究已经产生了大量的大量的实验数据，需要紧急整理信息提取，使用人工智能方法进行分析/建模以及数据/模型共享。计算型建模方法，特别是机器学习和深度学习方法，具有很高的应用价值。期望根据现有的信息开发 ENM 的预测模型性质/活性/毒性数据。目前 ENM 数据库不包含纳米结构注释以机器可读的格式存储各种结构信息，这一点至关重要用于计算建模研究。为了应对当前大数据时代的这一挑战，我们将开发一个大型的、公开可用的 ENM 门户，其中包含更多带注释的纳米结构超过3,000个ENM适合计算建模研究，这将导致合理的纳米医学设计。正在进行的纳米技术健康影响研究（NHIR）联盟正在为此门户的初始 ENM 数据库提供高质量的 ENM 数据，并且还将支持未来的数据管理。该数据库将基于虚拟设计纳米结构模拟（VINAS）技术，将注释复杂的纳米结构转换成适合机器学习建模目的的机器可读格式。到为此，我们将开发各种新的计算方法来注释纳米结构，特别是对于复杂的 ENM（例如石墨烯衍生物）。之后，我们将使用新机器学习和深度学习算法，例如加法模型和可解释的人工智能引导半监督深度学习技术，使用 ENM 数据开发预测模型精心策划的数据库作为概念证明。例如，虚拟纳米材料投影基于深度学习的方法，特别是可解释的人工智能引导的半监督方法生成对抗网络，将特别擅长处理带注释的纳米结构。在 VINAS 数据库门户网站中，作为最终可交付成果，精心策划的 ENM- 生物活性/特性/毒性数据和带注释的纳米结构将作为可下载共享文件供公共社区使用。由此产生的新的深度学习预测模型将是也分享了。这项研究为未来数据驱动的纳米信息学提供了一个新的公共平台建模研究，特别是那些基于机器学习的方法，可以极大地促进推进合理的纳米医学设计和现代纳米信息学的其他领域。