Virtual nanostructure simulation (VINAS) portal

虚拟纳米结构模拟 (VINAS) 门户

• 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
• 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

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.

消费者中使用纳米材料，尤其是工程的纳米材料（ENM） 在过去的十年中，产品和药品一直在飞涨。各种体外和体内 评估ENM的潜在环境和健康影响的研究产生了广泛的 数量的实验数据，需要紧急策划以进行信息提取， 分析/建模以及使用人工智能方法的数据/模型共享。计算 建模方法，尤其是机器学习和深度学习方法，具有很高的 根据可用的期望为ENM开发预测模型 财产/活动/毒性数据。当前ENMS数据库不包括纳米结构 以机器可读格式存储多种结构信息的注释，这些格式至关重要 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 开发一个大型公开的ENMS门户，其中包含更多的带注释的纳米结构 超过3,000个适合计算建模研究的ENM，这将导致理性 纳米医学设计。正在进行的纳米技术健康影响研究（NHIR） 财团正在为该门户的初始ENMS数据库提供高质量的ENMS数据和 还将支持未来的数据策划。该数据库将基于虚拟设计 纳米结构仿真（VINAS）技术，它将注释复杂的纳米结构 进入适合机器学习建模目的的机器可读格式。到 这一目标，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，例如加性模型和可解释的AI指导半 监督深度学习技术，以使用ENMS数据开发预测模型 策划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，尤其是可解释的AI指导的半手 生成的对抗网络将特别擅长处理注释 纳米结构。在VINAS数据库Web门户作为最终可交付成果中，策划的ENMS- 生物活性/属性/毒性数据和注释的纳米结构将作为可下载的 供公共社区使用的文件。由此产生的新的深度学习预测模型将是 也共享。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，尤其是基于机器学习的方法，并且可以极大地 推进理性纳米医学设计和现代纳米信息学的其他领域。