1
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Han Y, Deng M, Liu K, Chen J, Wang Y, Xu YN, Dian L. Computer-Aided Synthesis Planning (CASP) and Machine Learning: Optimizing Chemical Reaction Conditions. Chemistry 2024; 30:e202401626. [PMID: 39083362 DOI: 10.1002/chem.202401626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Revised: 07/27/2024] [Accepted: 07/28/2024] [Indexed: 08/02/2024]
Abstract
Computer-aided synthesis planning (CASP) has garnered increasing attention in light of recent advancements in machine learning models. While the focus is on reverse synthesis or forward outcome prediction, optimizing reaction conditions remains a significant challenge. For datasets with multiple variables, the choice of descriptors and models is pivotal. This selection dictates the effective extraction of conditional features and the achievement of higher prediction accuracy. This review delineates the origins of data in conditional optimization, the criteria for descriptor selection, the response models, and the metrics for outcome evaluation, aiming to acquaint readers with the latest research trends and facilitate more informed research in this domain.
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Affiliation(s)
- Yu Han
- State Key Laboratory of Microbial Technology, Institute of Microbial Technology, Shandong University, No. 72 Binhai Avenue, Qingdao, 266237, P. R. China
| | - Mingjing Deng
- State Key Laboratory of Microbial Technology, Institute of Microbial Technology, Shandong University, No. 72 Binhai Avenue, Qingdao, 266237, P. R. China
| | - Ke Liu
- State Key Laboratory of Microbial Technology, Institute of Microbial Technology, Shandong University, No. 72 Binhai Avenue, Qingdao, 266237, P. R. China
| | - Jia Chen
- State Key Laboratory of Microbial Technology, Institute of Microbial Technology, Shandong University, No. 72 Binhai Avenue, Qingdao, 266237, P. R. China
| | - Yuting Wang
- State Key Laboratory of Microbial Technology, Institute of Microbial Technology, Shandong University, No. 72 Binhai Avenue, Qingdao, 266237, P. R. China
| | - Yu-Ning Xu
- State Key Laboratory of Microbial Technology, Institute of Microbial Technology, Shandong University, No. 72 Binhai Avenue, Qingdao, 266237, P. R. China
| | - Longyang Dian
- State Key Laboratory of Microbial Technology, Institute of Microbial Technology, Shandong University, No. 72 Binhai Avenue, Qingdao, 266237, P. R. China
- Suzhou Institute of Shandong University, No. 388 Ruoshui Road, Suzhou Industrial Park, Suzhou, 215123, P. R. China
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2
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Gou Q, Liu J, Su H, Guo Y, Chen J, Zhao X, Pu X. Exploring an accurate machine learning model to quickly estimate stability of diverse energetic materials. iScience 2024; 27:109452. [PMID: 38523799 PMCID: PMC10960145 DOI: 10.1016/j.isci.2024.109452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 01/27/2024] [Accepted: 03/06/2024] [Indexed: 03/26/2024] Open
Abstract
High energy and low sensitivity have been the focus of developing new energetic materials (EMs). However, there has been a lack of a quick and accurate method for evaluating the stability of diverse EMs. Here, we develop a machine learning prediction model with high accuracy for bond dissociation energy (BDE) of EMs. A reliable and representative BDE dataset of EMs is constructed by collecting 778 experimental energetic compounds and quantum mechanics calculation. To sufficiently characterize the BDE of EMs, a hybrid feature representation is proposed by coupling the local target bond into the global structure characteristics. To alleviate the limitation of the low dataset, pairwise difference regression is utilized as a data augmentation with the advantage of reducing systematic errors and improving diversity. Benefiting from these improvements, the XGBoost model achieves the best prediction accuracy with R2 of 0.98 and MAE of 8.8 kJ mol-1, significantly outperforming competitive models.
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Affiliation(s)
- Qiaolin Gou
- College of Chemistry, Sichuan University, Chengdu 610064, China
| | - Jing Liu
- College of Chemistry, Sichuan University, Chengdu 610064, China
| | - Haoming Su
- College of Chemistry, Sichuan University, Chengdu 610064, China
| | - Yanzhi Guo
- College of Chemistry, Sichuan University, Chengdu 610064, China
| | - Jiayi Chen
- College of Chemistry, Sichuan University, Chengdu 610064, China
| | - Xueyan Zhao
- Institute of Chemical Materials, China Academy of Engineering Physics, Mianyang 621900, China
| | - Xuemei Pu
- College of Chemistry, Sichuan University, Chengdu 610064, China
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3
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Shi R, Yu G, Huo X, Yang Y. Prediction of chemical reaction yields with large-scale multi-view pre-training. J Cheminform 2024; 16:22. [PMID: 38403627 PMCID: PMC10895839 DOI: 10.1186/s13321-024-00815-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 02/14/2024] [Indexed: 02/27/2024] Open
Abstract
Developing machine learning models with high generalization capability for predicting chemical reaction yields is of significant interest and importance. The efficacy of such models depends heavily on the representation of chemical reactions, which has commonly been learned from SMILES or graphs of molecules using deep neural networks. However, the progression of chemical reactions is inherently determined by the molecular 3D geometric properties, which have been recently highlighted as crucial features in accurately predicting molecular properties and chemical reactions. Additionally, large-scale pre-training has been shown to be essential in enhancing the generalization capability of complex deep learning models. Based on these considerations, we propose the Reaction Multi-View Pre-training (ReaMVP) framework, which leverages self-supervised learning techniques and a two-stage pre-training strategy to predict chemical reaction yields. By incorporating multi-view learning with 3D geometric information, ReaMVP achieves state-of-the-art performance on two benchmark datasets. Notably, the experimental results indicate that ReaMVP has a significant advantage in predicting out-of-sample data, suggesting an enhanced generalization ability to predict new reactions. Scientific Contribution: This study presents the ReaMVP framework, which improves the generalization capability of machine learning models for predicting chemical reaction yields. By integrating sequential and geometric views and leveraging self-supervised learning techniques with a two-stage pre-training strategy, ReaMVP achieves state-of-the-art performance on benchmark datasets. The framework demonstrates superior predictive ability for out-of-sample data and enhances the prediction of new reactions.
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Affiliation(s)
- Runhan Shi
- Department of Computer Science and Engineering, and Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Gufeng Yu
- Department of Computer Science and Engineering, and Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Xiaohong Huo
- Shanghai Key Laboratory for Molecular Engineering of Chiral Drugs, Frontiers Science Center for Transformative Molecules, School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Yang Yang
- Department of Computer Science and Engineering, and Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
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4
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Yin X, Hsieh CY, Wang X, Wu Z, Ye Q, Bao H, Deng Y, Chen H, Luo P, Liu H, Hou T, Yao X. Enhancing Generic Reaction Yield Prediction through Reaction Condition-Based Contrastive Learning. RESEARCH (WASHINGTON, D.C.) 2024; 7:0292. [PMID: 38213662 PMCID: PMC10777739 DOI: 10.34133/research.0292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 12/06/2023] [Indexed: 01/13/2024]
Abstract
Deep learning (DL)-driven efficient synthesis planning may profoundly transform the paradigm for designing novel pharmaceuticals and materials. However, the progress of many DL-assisted synthesis planning (DASP) algorithms has suffered from the lack of reliable automated pathway evaluation tools. As a critical metric for evaluating chemical reactions, accurate prediction of reaction yields helps improve the practicality of DASP algorithms in the real-world scenarios. Currently, accurately predicting yields of interesting reactions still faces numerous challenges, mainly including the absence of high-quality generic reaction yield datasets and robust generic yield predictors. To compensate for the limitations of high-throughput yield datasets, we curated a generic reaction yield dataset containing 12 reaction categories and rich reaction condition information. Subsequently, by utilizing 2 pretraining tasks based on chemical reaction masked language modeling and contrastive learning, we proposed a powerful bidirectional encoder representations from transformers (BERT)-based reaction yield predictor named Egret. It achieved comparable or even superior performance to the best previous models on 4 benchmark datasets and established state-of-the-art performance on the newly curated dataset. We found that reaction-condition-based contrastive learning enhances the model's sensitivity to reaction conditions, and Egret is capable of capturing subtle differences between reactions involving identical reactants and products but different reaction conditions. Furthermore, we proposed a new scoring function that incorporated Egret into the evaluation of multistep synthesis routes. Test results showed that yield-incorporated scoring facilitated the prioritization of literature-supported high-yield reaction pathways for target molecules. In addition, through meta-learning strategy, we further improved the reliability of the model's prediction for reaction types with limited data and lower data quality. Our results suggest that Egret holds the potential to become an essential component of the next-generation DASP tools.
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Affiliation(s)
- Xiaodan Yin
- Dr. Neher’s Biophysics Laboratory for Innovative Drug Discovery, State Key Laboratory of Quality Research in Chinese Medicine,
Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macao 999078, China
- CarbonSilicon AI Technology Co. Ltd, Hangzhou, Zhejiang 310018, China
| | - Chang-Yu Hsieh
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Xiaorui Wang
- Dr. Neher’s Biophysics Laboratory for Innovative Drug Discovery, State Key Laboratory of Quality Research in Chinese Medicine,
Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macao 999078, China
- CarbonSilicon AI Technology Co. Ltd, Hangzhou, Zhejiang 310018, China
| | - Zhenxing Wu
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- CarbonSilicon AI Technology Co. Ltd, Hangzhou, Zhejiang 310018, China
| | - Qing Ye
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- CarbonSilicon AI Technology Co. Ltd, Hangzhou, Zhejiang 310018, China
| | - Honglei Bao
- Dr. Neher’s Biophysics Laboratory for Innovative Drug Discovery, State Key Laboratory of Quality Research in Chinese Medicine,
Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macao 999078, China
| | - Yafeng Deng
- CarbonSilicon AI Technology Co. Ltd, Hangzhou, Zhejiang 310018, China
| | - Hongming Chen
- Center of Chemistry and Chemical Biology,
Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou 510530, China
| | - Pei Luo
- Dr. Neher’s Biophysics Laboratory for Innovative Drug Discovery, State Key Laboratory of Quality Research in Chinese Medicine,
Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macao 999078, China
| | - Huanxiang Liu
- Faculty of Applied Sciences,
Macao Polytechnic University, Macao 999078, China
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Xiaojun Yao
- Faculty of Applied Sciences,
Macao Polytechnic University, Macao 999078, China
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5
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Dang HT, Porey A, Nand S, Trevino R, Manning-Lorino P, Hughes WB, Fremin SO, Thompson WT, Dhakal SK, Arman HD, Larionov OV. Kinetically-driven reactivity of sulfinylamines enables direct conversion of carboxylic acids to sulfinamides. Chem Sci 2023; 14:13384-13391. [PMID: 38033883 PMCID: PMC10685282 DOI: 10.1039/d3sc04727j] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 10/08/2023] [Indexed: 12/02/2023] Open
Abstract
Sulfinamides are some of the most centrally important four-valent sulfur compounds that serve as critical entry points to an array of emergent medicinal functional groups, molecular tools for bioconjugation, and synthetic intermediates including sulfoximines, sulfonimidamides, and sulfonimidoyl halides, as well as a wide range of other S(iv) and S(vi) functionalities. Yet, the accessible chemical space of sulfinamides remains limited, and the approaches to sulfinamides are largely confined to two-electron nucleophilic substitution reactions. We report herein a direct radical-mediated decarboxylative sulfinamidation that for the first time enables access to sulfinamides from the broad and structurally diverse chemical space of carboxylic acids. Our studies show that the formation of sulfinamides prevails despite the inherent thermodynamic preference for the radical addition to the nitrogen atom, while a machine learning-derived model facilitates prediction of the reaction efficiency based on computationally generated descriptors of the underlying radical reactivity.
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Affiliation(s)
- Hang T Dang
- Department of Chemistry, The University of Texas at San Antonio One UTSA Circle San Antonio TX 78249 USA
| | - Arka Porey
- Department of Chemistry, The University of Texas at San Antonio One UTSA Circle San Antonio TX 78249 USA
| | - Sachchida Nand
- Department of Chemistry, The University of Texas at San Antonio One UTSA Circle San Antonio TX 78249 USA
| | - Ramon Trevino
- Department of Chemistry, The University of Texas at San Antonio One UTSA Circle San Antonio TX 78249 USA
| | - Patrick Manning-Lorino
- Department of Chemistry, The University of Texas at San Antonio One UTSA Circle San Antonio TX 78249 USA
| | - William B Hughes
- Department of Chemistry, The University of Texas at San Antonio One UTSA Circle San Antonio TX 78249 USA
| | - Seth O Fremin
- Department of Chemistry, The University of Texas at San Antonio One UTSA Circle San Antonio TX 78249 USA
| | - William T Thompson
- Department of Chemistry, The University of Texas at San Antonio One UTSA Circle San Antonio TX 78249 USA
| | - Shree Krishna Dhakal
- Department of Chemistry, The University of Texas at San Antonio One UTSA Circle San Antonio TX 78249 USA
| | - Hadi D Arman
- Department of Chemistry, The University of Texas at San Antonio One UTSA Circle San Antonio TX 78249 USA
| | - Oleg V Larionov
- Department of Chemistry, The University of Texas at San Antonio One UTSA Circle San Antonio TX 78249 USA
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6
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Chen K, Chen G, Li J, Huang Y, Wang E, Hou T, Heng PA. MetaRF: attention-based random forest for reaction yield prediction with a few trails. J Cheminform 2023; 15:43. [PMID: 37038222 PMCID: PMC10084704 DOI: 10.1186/s13321-023-00715-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 03/21/2023] [Indexed: 04/12/2023] Open
Abstract
Artificial intelligence has deeply revolutionized the field of medicinal chemistry with many impressive applications, but the success of these applications requires a massive amount of training samples with high-quality annotations, which seriously limits the wide usage of data-driven methods. In this paper, we focus on the reaction yield prediction problem, which assists chemists in selecting high-yield reactions in a new chemical space only with a few experimental trials. To attack this challenge, we first put forth MetaRF, an attention-based random forest model specially designed for the few-shot yield prediction, where the attention weight of a random forest is automatically optimized by the meta-learning framework and can be quickly adapted to predict the performance of new reagents while given a few additional samples. To improve the few-shot learning performance, we further introduce a dimension-reduction based sampling method to determine valuable samples to be experimentally tested and then learned. Our methodology is evaluated on three different datasets and acquires satisfactory performance on few-shot prediction. In high-throughput experimentation (HTE) datasets, the average yield of our methodology's top 10 high-yield reactions is relatively close to the results of ideal yield selection.
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Affiliation(s)
- Kexin Chen
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, New Territories, Hong Kong SAR
| | | | | | - Yuansheng Huang
- College of Pharmaceutical Sciences, Zhejiang University, Zhejiang, China
| | - Ercheng Wang
- Zhejiang Lab, Zhejiang, China
- College of Pharmaceutical Sciences, Zhejiang University, Zhejiang, China
| | - Tingjun Hou
- College of Pharmaceutical Sciences, Zhejiang University, Zhejiang, China
| | - Pheng-Ann Heng
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, New Territories, Hong Kong SAR
- Zhejiang Lab, Zhejiang, China
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7
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Kee CW. Molecular Understanding and Practical In Silico Catalyst Design in Computational Organocatalysis and Phase Transfer Catalysis-Challenges and Opportunities. Molecules 2023; 28:1715. [PMID: 36838703 PMCID: PMC9966076 DOI: 10.3390/molecules28041715] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 02/03/2023] [Accepted: 02/05/2023] [Indexed: 02/25/2023] Open
Abstract
Through the lens of organocatalysis and phase transfer catalysis, we will examine the key components to calculate or predict catalysis-performance metrics, such as turnover frequency and measurement of stereoselectivity, via computational chemistry. The state-of-the-art tools available to calculate potential energy and, consequently, free energy, together with their caveats, will be discussed via examples from the literature. Through various examples from organocatalysis and phase transfer catalysis, we will highlight the challenges related to the mechanism, transition state theory, and solvation involved in translating calculated barriers to the turnover frequency or a metric of stereoselectivity. Examples in the literature that validated their theoretical models will be showcased. Lastly, the relevance and opportunity afforded by machine learning will be discussed.
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Affiliation(s)
- Choon Wee Kee
- Institute of Sustainability for Chemicals, Energy and Environment (ISCE2), Agency for Science, Technology and Research (A*STAR), 1 Pesek Road, Jurong Island, Singapore 627833, Republic of Singapore
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8
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Pan YZ, Xia Q, Zhu JX, Wang YC, Liang Y, Wang H, Tang HT, Pan YM. Electrochemically Mediated Carboxylative Cyclization of Allylic/Homoallylic Amines with CO 2 at Ambient Pressure. Org Lett 2022; 24:8239-8243. [DOI: 10.1021/acs.orglett.2c03377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Yong-Zhou Pan
- State Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources, School of Chemistry and Pharmaceutical Sciences of Guangxi Normal University, Guilin 541004, People’s Republic of China
| | - Qiang Xia
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin, Guangxi 541004, People’s Republic of China
| | - Jin-Xiu Zhu
- State Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources, School of Chemistry and Pharmaceutical Sciences of Guangxi Normal University, Guilin 541004, People’s Republic of China
| | - Ying-Chun Wang
- College of Chemistry and Chemical Engineering, Jishou University, Jishou 416000, China
| | - Ying Liang
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin, Guangxi 541004, People’s Republic of China
| | - Hengshan Wang
- State Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources, School of Chemistry and Pharmaceutical Sciences of Guangxi Normal University, Guilin 541004, People’s Republic of China
| | - Hai-Tao Tang
- State Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources, School of Chemistry and Pharmaceutical Sciences of Guangxi Normal University, Guilin 541004, People’s Republic of China
| | - Ying-Ming Pan
- State Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources, School of Chemistry and Pharmaceutical Sciences of Guangxi Normal University, Guilin 541004, People’s Republic of China
- College of Chemistry and Chemical Engineering, Jishou University, Jishou 416000, China
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9
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Li Z, Zhao L, Zhang Y, Yan H, Huang X, Shen G. Cascade Nucleophilic Attack/Addition Cyclization Reactions to Synthesize Oxazolidin-2-imines via ( Z)-2-Bromo-3-phenylprop-2-en-1-ols/3-phenylprop-2-yn-1-ols and Diphenyl Carbodiimides. J Org Chem 2022; 87:12721-12732. [PMID: 36099272 DOI: 10.1021/acs.joc.2c01268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Two concise strategies to synthesize oxazolidin-2-imines by cascade nucleophilic attack/addition cyclization reactions of (Z)-2-bromo-3-phenylprop-2-en-1-ols/3-phenylprop-2-yn-1-ols and diphenyl carbodiimides without a transition-metal catalyst have been developed. The reactions exhibited good substrate applicability tolerance, and a variety of substituted (Z)-4-((Z)-benzylidene)-N,3-diphenyloxazolidin-2-imines were synthesized in moderate to excellent yields with good stereoselectivity. The reports also provided a convenient strategy to synthesize 3-phenylprop-2-yn-1-ols by (Z)-2-bromo-3-phenylprop-2-en-1-ols. The economic and practical methods provide a great advantage for potential industrial synthesis of oxazolidin-2-imines.
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Affiliation(s)
- Zhanjun Li
- School of Chemistry and Chemical Engineering, School of Pharmaceutical Sciences, Liaocheng University, 1 Hunan Avenue, Liaocheng, Shandong 252000, P. R. China
| | - Lingyu Zhao
- Chemistry and Chemical Engineering, Jinan University, 106 Jiwei Road, Jinan, Shandong 250022, P. R. China
| | - Yalin Zhang
- School of Chemistry and Chemical Engineering, School of Pharmaceutical Sciences, Liaocheng University, 1 Hunan Avenue, Liaocheng, Shandong 252000, P. R. China
| | - Hui Yan
- School of Chemistry and Chemical Engineering, School of Pharmaceutical Sciences, Liaocheng University, 1 Hunan Avenue, Liaocheng, Shandong 252000, P. R. China
| | - Xianqiang Huang
- School of Chemistry and Chemical Engineering, School of Pharmaceutical Sciences, Liaocheng University, 1 Hunan Avenue, Liaocheng, Shandong 252000, P. R. China
| | - Guodong Shen
- School of Chemistry and Chemical Engineering, School of Pharmaceutical Sciences, Liaocheng University, 1 Hunan Avenue, Liaocheng, Shandong 252000, P. R. China.,Chemistry and Chemical Engineering, Jinan University, 106 Jiwei Road, Jinan, Shandong 250022, P. R. China
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10
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Hu KW, You X, Wen X, Yuan H, Xu QL, Lai Z. Synthesis of Functionalized Thiazolidin-2-imine and Oxazolidin-2-one Derivatives from p-Quinamines via [3 + 2] Annulation of Isothiocyanates and CO 2. J Org Chem 2022; 88:5052-5058. [PMID: 35880952 DOI: 10.1021/acs.joc.2c01031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
An efficient and environmentally friendly synthetic approach to prepare thiazolidine-2-imine and oxazolidine-2-one derivatives has been developed. Thiazolidine-2-imines are synthesized in good to excellent yields by [3 + 2] annulation of p-quinamines with isothiocyanates under catalyst- and solvent-free conditions. Oxazolidine-2-ones are produced in good to excellent yields via [3 + 2] annulation of p-quinamines with CO2 using triethylenediamine (DABCO) as an organocatalyst. Furthermore, this strategy can be performed on a gram scale and tolerate a wide range of functional groups.
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Affiliation(s)
- Kai-Wen Hu
- School of Life Sciences and Health Engineering, Jiangnan University,1800 Lihu Avenue, 214122, Wuxi, China.,Jiangsu Key Laboratory of Drug Discovery for Metabolic Diseases and State Key Laboratory of Natural Medicines, China Pharmaceutical University, 24 Tongjia Xiang, Nanjing 210009, China
| | - Xiao You
- Jiangsu Key Laboratory of Drug Discovery for Metabolic Diseases and State Key Laboratory of Natural Medicines, China Pharmaceutical University, 24 Tongjia Xiang, Nanjing 210009, China
| | - Xiaoan Wen
- Jiangsu Key Laboratory of Drug Discovery for Metabolic Diseases and State Key Laboratory of Natural Medicines, China Pharmaceutical University, 24 Tongjia Xiang, Nanjing 210009, China
| | - Haoliang Yuan
- Jiangsu Key Laboratory of Drug Discovery for Metabolic Diseases and State Key Laboratory of Natural Medicines, China Pharmaceutical University, 24 Tongjia Xiang, Nanjing 210009, China
| | - Qing-Long Xu
- Jiangsu Key Laboratory of Drug Discovery for Metabolic Diseases and State Key Laboratory of Natural Medicines, China Pharmaceutical University, 24 Tongjia Xiang, Nanjing 210009, China
| | - Zengwei Lai
- School of Life Sciences and Health Engineering, Jiangnan University,1800 Lihu Avenue, 214122, Wuxi, China
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11
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Yang L, Zhu L, Zhang S, Hong X. Machine Learning Prediction of
Structure‐Performance
Relationship in Organic Synthesis. CHINESE J CHEM 2022. [DOI: 10.1002/cjoc.202200039] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Li‐Cheng Yang
- Center of Chemistry for Frontier Technologies, Department of Chemistry, State Key Laboratory of Clean Energy Utilization, Zhejiang University Hangzhou Zhejiang 310027 China
| | - Lu‐Jing Zhu
- Center of Chemistry for Frontier Technologies, Department of Chemistry, State Key Laboratory of Clean Energy Utilization, Zhejiang University Hangzhou Zhejiang 310027 China
| | - Shuo‐Qing Zhang
- Center of Chemistry for Frontier Technologies, Department of Chemistry, State Key Laboratory of Clean Energy Utilization, Zhejiang University Hangzhou Zhejiang 310027 China
| | - Xin Hong
- Center of Chemistry for Frontier Technologies, Department of Chemistry, State Key Laboratory of Clean Energy Utilization, Zhejiang University Hangzhou Zhejiang 310027 China
- Beijing National Laboratory for Molecular Sciences, Zhongguancun North First Street NO. 2 Beijing 100190 China
- Key Laboratory of Precise Synthesis of Functional Molecules of Zhejiang Province, School of Science, Westlake University, 18 Shilongshan Road Hangzhou Zhejiang 310024 China
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12
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Yekke-Ghasemi Z, Heravi MM, Malmir M, Jahani G, Bisafar MB, Mirzaei M. Fabrication of heterogeneous-based lacunary polyoxometalates as efficient catalysts for the multicomponent and clean synthesis of pyrazolopyranopyrimidines. INORG CHEM COMMUN 2022. [DOI: 10.1016/j.inoche.2022.109456] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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13
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Yang Q, Liu Y, Cheng J, Li Y, Liu S, Duan Y, Zhang L, Luo S. An Ensemble Structure and Physiochemical (SPOC) Descriptor for Machine-Learning Prediction of Chemical Reaction and Molecular Properties. Chemphyschem 2022; 23:e202200255. [PMID: 35478429 DOI: 10.1002/cphc.202200255] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Indexed: 11/08/2022]
Abstract
Feature representations, or descriptors, are machines' chemical language that largely shapes the prediction capability, generalizability and interpretability of machine learning models. To develop a generally applicable descriptor is highly warranted for chemists to deal with conventional prediction tasks in the context of sparsely distributed and small datasets. Inspired by the chemist's vision on molecules, we presented herein an ensemble descriptor, SPOC, curated on the principles of physical organic chemistry that integrates Structure and Physicochemical property (SPOC) of a molecule. SPOC could be readily constructed by combining molecular fingerprints, representing the structure of a given molecule, and molecular physicochemical properties extracted from RDKit or Mordred molecular descriptors. The applicability of SPOC was fully surveyed in a range of well-structured chemical databases with machine learning tasks varying from regression to classifications.
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Affiliation(s)
- Qi Yang
- Tsinghua University, CBMS, Department of Chemistry, CHINA
| | - Yidi Liu
- Tsinghua University, CBMS, Department of Chemistry, CHINA
| | - Junjie Cheng
- Tsinghua University, CBMS, Department of Chemistry, CHINA
| | - Yao Li
- Tsinghua University, CBMS, Department of Chemistry, CHINA
| | - Siyuan Liu
- Tsinghua University, CBMS, Department of Chemistry, CHINA
| | - Yingdong Duan
- Tsinghua University, CBMS, Department of Chemistry, CHINA
| | - Long Zhang
- Tsinghua University, CBMS, Department of Chemistry, CHINA
| | - Sanzhong Luo
- Tsinghua University, Department of Chemistry, Tsinghua University, 100084, Beijing, CHINA
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Lu J, Zhang Y. Unified Deep Learning Model for Multitask Reaction Predictions with Explanation. J Chem Inf Model 2022; 62:1376-1387. [PMID: 35266390 PMCID: PMC8960360 DOI: 10.1021/acs.jcim.1c01467] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
There is significant interest and importance to develop robust machine learning models to assist organic chemistry synthesis. Typically, task-specific machine learning models for distinct reaction prediction tasks have been developed. In this work, we develop a unified deep learning model, T5Chem, for a variety of chemical reaction predictions tasks by adapting the "Text-to-Text Transfer Transformer" (T5) framework in natural language processing (NLP). On the basis of self-supervised pretraining with PubChem molecules, the T5Chem model can achieve state-of-the-art performances for four distinct types of task-specific reaction prediction tasks using four different open-source data sets, including reaction type classification on USPTO_TPL, forward reaction prediction on USPTO_MIT, single-step retrosynthesis on USPTO_50k, and reaction yield prediction on high-throughput C-N coupling reactions. Meanwhile, we introduced a new unified multitask reaction prediction data set USPTO_500_MT, which can be used to train and test five different types of reaction tasks, including the above four as well as a new reagent suggestion task. Our results showed that models trained with multiple tasks are more robust and can benefit from mutual learning on related tasks. Furthermore, we demonstrated the use of SHAP (SHapley Additive exPlanations) to explain T5Chem predictions at the functional group level, which provides a way to demystify sequence-based deep learning models in chemistry. T5Chem is accessible through https://yzhang.hpc.nyu.edu/T5Chem.
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Affiliation(s)
- Jieyu Lu
- Department of Chemistry, New York University, New York, New York 10003, United States
| | - Yingkai Zhang
- Department of Chemistry, New York University, New York, New York 10003, United States
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China
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