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

美国密苏里大学哥伦比亚分校的林健教授和Chao Shih-Kang教授以及卡内基梅隆大学的Olexandr Isayev教授获得了化学学部化学理论、模型和计算方法（CTMC）项目的奖励。他们将开发并应用一种新的数据驱动架构来设计具有所需物理和化学性质的新分子。该项目结合了生成建模、强化学习和主动学习算法，提供了一种通用的方法来解决属性反对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，为探索通过简单优化现有分子无法实现的新化学空间提供新的途径，并为生成模型如何学习化学原理提供理解。设计的具有多种优化性能的新分子，如物理化学，电子，光学，氧化还原性能，将改变医学，光伏，催化，储热和有机氧化还原液流电池的各种应用。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，以及通过外展项目改善K12学校的STEM教育，促进STEM领域和未来劳动力的多样性。Lin、Chao和Isayev教授将展示一个数据驱动的闭环框架，用于在极端范围内重新生成具有所需物理化学性质的新分子。提出的研究是由分子生成继承的三个主要挑战驱动的：(i)产生具有靶向和可量化特性的新分子；（ii）生成满足多个属性目标的分子；（iii）生成的分子具有超出训练数据集范围的目标属性。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，该方法结合了强化学习和条件生成对抗网络，以设计具有多种特性的新分子。研究团队将把管道与主动学习结合起来，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展。该奖项反映了美国国家科学基金会的法定使命，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。

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.