Collaborative Research: III: Medium: New Machine Learning Empowered Nanoinformatics System for Advancing Nanomaterial Design

合作研究：III：媒介：新的机器学习赋能纳米信息学系统，促进纳米材料设计

• 批准号: 2402311
• 负责人: Hao Zhu
• 金额: $ 35万
• 依托单位: Tulane University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2402311&HistoricalAwards=false
• 关键词: Collaborative; Research; III; Medium; New

The research objective of this proposal is to address the computational challenges in the innovative nanomaterial data analysis or nanoinformatics for predicting nanomaterials properties. Nanomaterials are very small materials that can be used in a variety of applications, including nanomedicine development. The vast quantities of existing experimental data require new nanoinformatics approaches and toolkits for data extraction, analysis, and sharing. This can help guide the safe design of next-generation of nanomedicines with desirable therapeutic activities, while also ensuring they have limited side effects. However, there are currently two critical limitations to using machine learning approaches in nanoinformatics modeling studies. First, most existing data available for modeling were based on a limited number of nanomaterials that also have limited experimental characterization of their chemical properties. Second, despite significant efforts from various researchers, the available modeling approaches that have been developed are applicable only for a specified small set of nanomaterials and have rarely been used to design nanomaterials. This project will address the computational challenges in large-scale nanomaterial data mining, development and validation of an automated informatics framework to digitalize nanostructures, identify molecular markers, and support fast nanomaterial retrieval and integrative analysis. This project will also facilitate the development of novel educational tools to enhance several current courses at Rutgers University, University of Pittsburgh, and University of Minnesota. The investigators will engage the minority students and under-served populations in research activities to give them a better exposure to cutting-edge science research.In this project, a novel machine learning based nanoinformatics framework will be developed to integrate new digital nanostructure representations with the emerging key computational techniques. The project focuses on designing principled machine learning and data science algorithms for analyzing large-scale nanomaterial data to create new informatics toolkits to facilitate the nanomedicine-based treatments and new nanomaterial design. Specifically, the following research goals will be met in this project: 1) new computational tools to automate nanostructure digitalization; 2) interpretation method to enhance deep learning based predictive models; 3) new cross-modal deep hashing network for fast and accurate nanomaterial data retrieval; and 4) evaluate the proposed methods and system using real large-scale nanomaterial data and release the database and nanoinformatics toolkits to the public. Unlike most existing nanoinformatics strategies that perform modeling and analysis at a small scale, this project will provide promising new directions to the analysis of large-scale complex nanomaterial data by addressing the critical data-intensive analysis issues including efficiency, scalability, and interpretability. The investigations combine rigorous theoretical analysis and emerging application studies and will contribute to both academic research and potential commercialized products. This project will advance and thus extend the relationship between engineering innovation and computational analysis, and hold great promise for nanomaterial and nanomedicine developments.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

本提案的研究目标是解决创新纳米材料数据分析或纳米信息学预测纳米材料特性的计算挑战。纳米材料是非常小的材料，可用于各种应用，包括纳米医学开发。大量现有实验数据需要新的纳米信息学方法和工具包来进行数据提取、分析和共享。这可以帮助指导具有理想治疗活性的下一代纳米药物的安全设计，同时确保它们具有有限的副作用。然而，目前在纳米信息学建模研究中使用机器学习方法存在两个关键限制。首先，大多数可用于建模的现有数据都是基于数量有限的纳米材料，这些纳米材料的化学性质的实验表征也有限。其次，尽管各个研究人员做出了巨大努力，但已开发的可用建模方法仅适用于指定的一小部分纳米材料，并且很少用于设计纳米材料。该项目将解决大规模纳米材料数据挖掘，自动信息学框架的开发和验证中的计算挑战，以数字化纳米结构，识别分子标记，并支持快速纳米材料检索和综合分析。该项目还将促进开发新的教育工具，以加强罗格斯大学、匹兹堡大学和明尼苏达大学目前的几门课程。研究人员将让少数民族学生和服务不足的人群参与研究活动，让他们更好地接触前沿科学研究。在这个项目中，将开发一种新的基于机器学习的纳米信息学框架，将新的数字纳米结构表示与新兴的关键计算技术相结合。该项目的重点是设计有原则的机器学习和数据科学算法，用于分析大规模纳米材料数据，以创建新的信息学工具包，促进基于纳米医学的治疗和新的纳米材料设计。具体而言，该项目将实现以下研究目标：1）自动化纳米结构数字化的新计算工具; 2）增强基于深度学习的预测模型的解释方法; 3）快速准确检索纳米材料数据的新跨模态深度哈希网络;以及4）使用真实的大规模纳米材料数据评估所提出的方法和系统，并向公众发布数据库和纳米信息学工具包。与大多数现有的在小规模上进行建模和分析的纳米信息学策略不同，该项目将通过解决关键的数据密集型分析问题，包括效率，可扩展性和可解释性，为大规模复杂纳米材料数据的分析提供有前途的新方向。这些调查联合收割机结合了严格的理论分析和新兴的应用研究，将有助于学术研究和潜在的商业化产品。该项目将推进并扩展工程创新和计算分析之间的关系，并为纳米材料和纳米医学的发展提供巨大的希望。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。