SusChEM: Machine learning blueprints for greener chelants

SusChEM：绿色螯合剂的机器学习蓝图

• 批准号: 1705592
• 负责人: John Keith
• 金额: $ 30万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1705592&HistoricalAwards=false
• 关键词: SusChEM; Machine; learning; blueprints; greener

1705592 (Keith). Chelating agents have recently been identified as a key category of chemical products that are ripe for greener design. It is hypothesized that identifying better alternatives will require far broader explorations of chemical compound space than what is possible with conventional trial and error experimentation. In this project, accurate quantum chemistry calculations will be used to train state-of-the-art machine learning methods that will allow prediction of structures of greener chelating agents.The machine learning method that will be developed promises a novel route to rapidly predict properties of chelant/metal complexes, not only with higher accuracy but with six orders of magnitude less computational time than conventional predictive quantum chemistry methods (e.g. Kohn-Sham Density Functional Theory). With this computational tool, it will be possible to rapidly screen through about 100,000 hypothetical chelant structures to predict those that would bind strongly to different metal ions. The project will also screen these complexes to see which have high propensities to degrade in reasonable timeframes, and which have low probabilities of being toxic. The top candidates from this novel screening approach will then be experimentally synthesized and tested. This will validate if quantum chemistry-based machine learning would be a transformative tool for environmental sustainability and green chemical design by being a more predictive supplement and/or alternative to conventional QSAR models. Four basic scientific questions will be addressed by the project. First, state-of-the-art computational quantum chemistry will be used to develop a quantitative understanding of which chelant/metal complex properties (bond energies, pKas, etc.) best correlate with overall chelant stability constants in aqueous solutions. Second, machine learning methods will be developed to be used to drive in silico searches for non-traditional molecular structures that would be able to bind as strong (or stronger) to different metal ions as EDTA. Third, additional computational screening procedures will then be used to find which of these promising chelant structures are unlikely to be non-toxic and nonpersistent in nature. Finally, it will be experimentally validated which of the novel chelants identified via computation would be industrially synthesized and economically viable for widespread use. If successful, this research effort would signify a paradigm shift for computer-aided design of greener chelants used in detergents, treatments of heavy metal poisoning, metal extraction for soil treatments, waste remediation, sequestering normally occurring radioactive materials from hydraulic fracturing sites, and water purification. This project will lay important foundational work that is needed to introduce new state-of-the-art computational modeling tools with greater predictive capacity than widely used QSAR models. All developed computer programs and accompanying tutorials for how to use the programs will be made freely available on the website of the PI. The educational component of this project will develop a computer game, "Chelate-it", which will allow students to quantify different fundamental chemical bonding concepts involved in chelation and then use that knowledge to design novel chelant structures on their own. The computer game will be tested in a summer school program at the University of Pittsburgh for underrepresented 10th grade students. The capacity for the computer game to educate students about chemical bonding, environmental sustainability engineering, and research will be assessed.

1705592（基思）。螯合剂最近已被确定为一个关键类别的化学产品，是成熟的绿色设计。据推测，确定更好的替代品将需要更广泛的探索化学化合物的空间比什么是可能与传统的试错实验。在这个项目中，精确的量子化学计算将被用于训练最先进的机器学习方法，这将允许预测更绿色的螯合剂的结构。将开发的机器学习方法有望成为快速预测螯合剂/金属络合物性质的新途径，不仅具有更高的准确性，而且计算时间比传统的预测量子化学方法少六个数量级（例如Kohn-Sham密度泛函理论）。有了这个计算工具，将有可能快速筛选大约100，000种假设的螯合剂结构，以预测那些与不同金属离子强烈结合的结构。该项目还将筛选这些复合物，以确定哪些在合理的时间范围内具有高降解倾向，哪些具有低毒性概率。然后，将通过实验合成和测试这种新型筛选方法的顶级候选人。这将验证基于量子化学的机器学习是否会成为环境可持续性和绿色化学设计的变革性工具，因为它是传统QSAR模型的更具预测性的补充和/或替代。该项目将解决四个基本科学问题。首先，将使用最先进的计算量子化学来定量了解螯合剂/金属络合物的性质（键能，pKa等）。最好与水溶液中的总螯合剂稳定常数相关。其次，将开发机器学习方法，用于驱动计算机搜索非传统分子结构，这些结构能够与EDTA一样强（或更强）地与不同的金属离子结合。第三，额外的计算筛选程序，然后将被用来找到这些有前途的螯合剂结构是不可能是无毒和非持久性的性质。最后，将通过实验验证通过计算鉴定的新型螯合剂中的哪一种将在工业上合成并且经济上可行以广泛使用。如果成功，这项研究工作将意味着计算机辅助设计的绿色螯合剂的范式转变，用于洗涤剂，重金属中毒的治疗，土壤处理的金属提取，废物修复，从水力压裂现场隔离通常发生的放射性物质，以及水净化。该项目将奠定重要的基础工作，需要引进新的国家的最先进的计算建模工具，具有更大的预测能力比广泛使用的QSAR模型。所有开发的计算机程序和附带的如何使用程序的教程将在PI的网站上免费提供。该项目的教育部分将开发一个电脑游戏“螯合它”，让学生量化螯合作用中涉及的不同基本化学键合概念，然后利用这些知识自行设计新的螯合剂结构。这款电脑游戏将在匹兹堡大学为代表性不足的10年级学生举办的暑期学校项目中进行测试。将评估计算机游戏教育学生化学键合，环境可持续性工程和研究的能力。

项目成果

Machine Learning-Guided Approach for Studying Solvation Environments

• DOI: 10.1021/acs.jctc.9b00605
• 发表时间: 2020-01-01
• 期刊: JOURNAL OF CHEMICAL THEORY AND COMPUTATION
• 影响因子: 5.5
• 作者: Basdogan, Yasemin;Groenenboom, Mitchell C.;Keith, John A.
• 通讯作者: Keith, John A.

Computationally Guided Searches for Efficient Catalysts through Chemical/Materials Space: Progress and Outlook

通过化学/材料空间计算引导寻找高效催化剂：进展与展望

• DOI: 10.1021/acs.jpcc.0c11345
• 发表时间: 2021
• 期刊: The Journal of Physical Chemistry C
• 作者: Griego, Charles D.;Maldonado, Alex M.;Zhao, Lingyan;Zulueta, Barbaro;Gentry, Brian M.;Lipsman, Eli;Choi, Tae Hoon;Keith, John A.
• 通讯作者: Keith, John A.

Advances and challenges in modeling solvated reaction mechanisms for renewable fuels and chemicals

• DOI: 10.1002/wcms.1446
• 发表时间: 2019-10-17
• 期刊: WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE
• 影响因子: 11.4
• 作者: Basdogan, Yasemin;Maldonado, Alex M.;Keith, John A.
• 通讯作者: Keith, John A.

Computational predictions of metal–macrocycle stability constants require accurate treatments of local solvent and pH effects

金属大环稳定性常数的计算预测需要准确处理局部溶剂和 pH 影响

• DOI: 10.1039/d1cp00611h
• 发表时间: 2021
• 期刊: Physical Chemistry Chemical Physics
• 影响因子: 3.3
• 作者: Gentry, Brian M.;Choi, Tae Hoon;Belfield, William S.;Keith, John A.
• 通讯作者: Keith, John A.

First-principles modeling of chemistry in mixed solvents: Where to go from here?

• DOI: 10.1063/1.5143207
• 发表时间: 2020-04
• 期刊: The Journal of chemical physics
• 作者: Alex M. Maldonado;Yasemin Basdogan;J. T. Berryman;S. Rempe;J. Keith