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.

密苏里大学哥伦比亚分校的林健教授和赵世康教授以及卡内基梅隆大学的Olexandr Isayev教授获得了化学系化学理论，模型和计算方法（CTMC）项目的奖项。他们将开发和应用一种新的数据驱动架构，用于设计具有所需物理和化学性质的新型分子。该项目结合了生成建模，强化学习和主动学习算法，以提供一种通用方法来解决针对属性的逆分子设计的长期科学挑战。该方法将提高对分子表征的理解，为探索现有分子的简单优化无法达到的新化学空间提供新的途径，并提供对生成模型如何学习化学原理的理解。所设计的具有多种优化性质的新型分子，例如物理化学、电子、光学、氧化还原性质，将改变在医学、光化学、催化、热存储和有机氧化还原液流电池中的各种应用。此外，该项目的跨学科性质将为参与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的研究经验。该项目还将通过增加STEM学科的女性人数，以及通过外展计划改善K12学校的STEM教育，促进STEM领域和未来劳动力的多样性。林教授，赵教授和Isayev教授将展示一个数据驱动的闭环框架，用于从头生成具有极端范围所需物理化学性质的新型分子。提出的研究的动机是分子生成中继承的三个主要挑战：（i）生成具有靶向和可量化特性的新型分子;（ii）生成满足多个特性目标的分子;（iii）生成具有超出训练数据集范围的靶向特性的分子。为了应对这些挑战，该合作团队将开发一种集成的数据驱动方法，该方法将强化学习和条件生成对抗网络相结合，以设计具有目标多种特性的新型分子。该研究团队将联合收割机与主动学习相结合，以实现迭代的闭环分子开发过程，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。

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.

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

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