Collaborative Research: A Data-driven Closed-loop Framework for De Novo Generation of Molecules with Targeted Properties

协作研究：用于从头生成具有目标特性的分子的数据驱动闭环框架

• 批准号: 2154428
• 负责人: Jian Lin
• 金额: $ 36.08万
• 依托单位: University of Missouri-Columbia
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2154428&HistoricalAwards=false
• 关键词: Collaborative; Research; Data; driven; Closed

Professors Jian Lin and Shih-Kang Chao of University of Missouri-Columbia and Olexandr Isayev of Carnegie Mellon University are supported by an award from the Chemical Theory, Models and Computational Methods (CTMC) program in the Division of Chemistry. They will develop and apply a novel data-driven architecture for designing novel molecules with desired physical and chemical properties. The project combines generative modeling, reinforcement learning and active learning algorithms to afford a general methodology to solve a long-lasting scientific challenge of property-objected inverse molecular design. The methodology will improve understanding of molecular representations, provide a new route to exploring novel chemical space inaccessible by simple optimization of existing molecules, and provide understanding on how the generative model learns chemical principles. The designed novel molecules with multiple optimized properties, e.g. physicochemical, electronic, optical, redox properties, will transform a variety of applications in medicine, photovoltaics, catalysis, thermal storage, and organic redox flow batteries. In addition, the interdisciplinary nature of this project will offer the research experience in chemistry, materials science, statistics, and computer science to involved undergraduate and graduate students. The project will also promote diversity in the STEM fields and future workforce by increasing females in STEM disciplines as well as improving STEM education in K12 school via outreach programs.Professors Lin, Chao, and Isayev will demonstrate a data-driven closed-loop framework for de novo generation of novel molecules with desired physicochemical properties in the extreme range. The proposed research is motivated by three main challenges inherited in molecule generation: (i) generation of novel molecules with targeted and quantifiable properties; (ii) generation of molecules meeting multiple property objectives; (iii) generated molecules having targeted properties beyond the range in the training dataset. To tackle these challenges, this collaborative team will develop an integrated data-driven methodology that combines a reinforced learning and conditional generative adversarial network to design novel molecules with targeted multiple properties. The research team will combine the pipeline with active learning to enable an iterative close-loop molecular development process, which will accelerate scientific progress in molecular discovery.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.

密苏里州哥伦比亚大学的Jian Lin和Shih-Kang Chao和Carnegie Mellon大学的Olexandr Isayev得到了化学理论，模型和计算方法（CTMC）计划的奖项。他们将开发并应用新型的数据驱动结构，以设计具有所需物理和化学特性的新分子。该项目结合了生成型建模，增强学习和主动学习算法，以提供一种通用方法，以解决对属性逆向分子设计的持久科学挑战。该方法将提高对分子表示的理解，为探索新的化学空间的新途径通过简单优化现有分子而无法访问，并提供对生成模型如何学习化学原理的理解。具有多种优化特性的设计新分子，例如物理化学，电子，光学，氧化还原特性，将改变药物，光伏，催化，热存储和有机氧化还原流量电池的各种应用。此外，该项目的跨学科性质将为涉及的本科生和研究生提供化学，材料科学，统计学和计算机科学方面的研究经验。该项目还将通过扩大STEM学科中的女性以及通过外展计划来改善K12学校的STEM教育，并通过外展计划来促进多样性。ProfessorsLin，Chao和Isayev将展示数据驱动的封闭环形框架，用于De Novo Novo Generacuules的新型分子，具有所需的物理学物质范围，具有极端的极端物理范围。提出的研究是由分子生成中遗传的三个主要挑战激励的：（i）具有靶向和可量化特性的新分子的产生； （ii）符合多个财产目标的分子的产生； （iii）产生的分子具有超出训练数据集范围的靶向性能。为了应对这些挑战，这个协作团队将开发一种集成的数据驱动方法，该方法结合了增强的学习和有条件的生成对抗网络，以设计具有针对性的多个特性的新分子。研究团队将将管道与积极学习相结合，以实现迭代近环分子发展过程，这将加速分子发现的科学进步。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的审查标准来通过评估来获得支持的。

项目成果

Scientific machine learning framework to understand flash graphene synthesis

理解闪存石墨烯合成的科学机器学习框架

• 发表时间: 2023
• 期刊: arXivorg
• 作者: Sattari, K.;Beckham, J. L.;Eddy, L.;Wyss, K. M.;Byfield, R.;Tour, J. M.;Lin, J.
• 通讯作者: Lin, J.

Toward Autonomous Laboratories: Convergence of Artificial Intelligence and Experimental Automation

• DOI: 10.1016/j.pmatsci.2022.101043
• 发表时间: 2022-11
• 期刊: Progress in Materials Science
• 影响因子: 37.4
• 作者: Yunchao Xie;Kianoosh Sattari;Chi Zhang;Jian Lin

De Novo Design of Molecules Towards Biased Properties via a Deep Generative Framework and Iterative Transfer Learning

• 发表时间: 2023
• 期刊: Materials & Design
• 影响因子: 8.4
• 作者: Kianoosh Sattari;Dawei Li;Yunchao Xie;O. Isayev;Jian Lin