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.

该提案的研究目标是解决用于预测纳米材料特性的创新纳米材料数据分析或纳米信息学中的计算挑战。纳米材料是非常小的材料，可用于多种应用，包括纳米医学开发。现有的大量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。这有助于指导具有理想治疗活性的下一代纳米药物的安全设计，同时确保它们的副作用有限。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有限数量的纳米材料，这些材料的化学性质的实验表征也有限。其次，尽管各个研究人员付出了巨大的努力，但已开发的可用建模方法仅适用于特定的一小组纳米材料，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、开发和验证自动化信息学框架中的计算挑战，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。该项目还将促进新型教育工具的开发，以增强罗格斯大学、匹兹堡大学和明尼苏达大学当前的几门课程。研究人员将让少数族裔学生和服务不足的人群参与研究活动，让他们更好地接触尖端科学研究。在这个项目中，将开发一种基于机器学习的新型纳米信息学框架，将新的数字纳米结构表示与新兴的关键计算技术。该项目的重点是设计原则性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，以促进基于纳米医学的治疗和新的纳米材料设计。具体来说，该项目将实现以下研究目标：1）实现纳米结构数字化自动化的新计算工具； 2）增强基于深度学习的预测模型的解释方法； 3）新的跨模态深度哈希网络，用于快速准确的纳米材料数据检索； 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。与大多数现有的小规模建模和分析的纳米信息学策略不同，该项目将通过解决包括效率、可扩展性和可解释性在内的关键数据密集型分析问题，为大规模复杂纳米材料数据的分析提供有前景的新方向。这些研究结合了严格的理论分析和新兴的应用研究，将为学术研究和潜在的商业化产品做出贡献。该项目将推进并从而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带来巨大希望。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查进行评估，被认为值得支持标准。