Papers citing Prediction of Major Regio‐, Site‐, and Diastereoisomers in Diels–Alder Reactions by Using Machine‐Learning: The Importance of Physically Meaningful Descriptors
Towards Data‐Driven Design of Asymmetric Hydrogenation of Olefins: Database and Hierarchical Learning
Angewandte Chemie · 2021-08-09
Li‐Cheng Xu, Shuo‐Qing Zhang, Xin Li, Miao‐Jiong Tang, Pei‐Pei Xie, Xin Hong
Read original paper ↗ ·National Institutes of Health (NIH) Workshop on Reaction Informatics
ChemRxiv · 2021-08-19
Wendy A. Warr
Read original paper ↗ ·Machine learning accelerates quantum mechanics predictions of molecular crystals
Physics Reports · 2021-08-24
Yanqiang Han, Imran Ali, Zhilong Wang, Junfei Cai, Sicheng Wu, Jiequn Tang, Lin Zhang, Jiahao Ren, Rui Xiao, Qianqian Lu, Lei Hang, Hongyuan Luo, Jinjin Li
Read original paper ↗ ·Is Organic Chemistry Really Growing Exponentially?
Angewandte Chemie International Edition · 2021-09-24
Sara Szymkuć, Tomasz Badowski, Bartosz A. Grzybowski
Read original paper ↗ ·Is Organic Chemistry Really Growing Exponentially?
Angewandte Chemie · 2021-09-24
Sara Szymkuć, Tomasz Badowski, Bartosz A. Grzybowski
Read original paper ↗ ·Study of the Relationship between Synthesis Descriptors and the Type of Zeolite Phase Formed in ZSM‐43 Synthesis by Using Machine Learning
ChemistrySelect · 2021-10-18
Ganesan Raman
Read original paper ↗ ·Machine Learning Applied to the Modeling of Pharmacological and ADMET Endpoints
Methods in molecular biology · 2021-11-03
Andreas H. Göller, Lara Kuhnke, Antonius ter Laak, Katharina Meier, Alexander Hillisch
Read original paper ↗ ·Optical deciphering of multinary chiral compound mixtures through organic reaction based chemometric chirality sensing
Nature Communications · 2021-11-08
Diandra S. Hassan, Christian Wolf
Read original paper ↗ ·A machine learning approach for predicting the nucleophilicity of organic molecules
Physical Chemistry Chemical Physics · 2021-12-06
Vaneet Saini, Aditya Sharma, Dhruv Nivatia
Read original paper ↗ ·Predicting reaction conditions from limited data through active transfer learning
Chemical Science · 2022-01-01
Eunjae Shim, Joshua A. Kammeraad, Ziping Xu, Ambuj Tewari, Tim Cernak, Paul M. Zimmerman
Read original paper ↗ ·RegioML: predicting the regioselectivity of electrophilic aromatic substitution reactions using machine learning
Digital Discovery · 2022-01-01
Nicolai Ree, Andreas H. Göller, Jan Halborg Jensen
Read original paper ↗ ·The effect of chemical representation on active machine learning towards closed-loop optimization
Reaction Chemistry & Engineering · 2022-01-01
Alexander Pomberger, A. A. Pedrina McCarthy, A. Khan, Simon Sung, Connor J. Taylor, Matthew J. Gaunt, Lucy J. Colwell, D. Walz, Alexei A. Lapkin
Read original paper ↗ ·Routescore: Punching the Ticket to More Efficient Materials Development
ACS Central Science · 2022-01-06
Martin T. Seifrid, Riley J. Hickman, Andrés Aguilar‐Granda, Cyrille Lavigne, Jenya Vestfrid, Tony Wu, Théophile Gaudin, Emily J. Hopkins, Alán Aspuru‐Guzik
Read original paper ↗ ·Regioselectivity Switch Towards the Development of Innovative Diels‐Alder Cycloaddition and Productive Applications in Organic Synthesis
Asian Journal of Organic Chemistry · 2022-02-07
Amar Nath Singh Chauhan, Ghanshyam Mali, Rohan D. Erande
Read original paper ↗ ·Quantum chemistry-augmented neural networks for reactivity prediction: Performance, generalizability, and explainability
The Journal of Chemical Physics · 2022-02-22
Thijs Stuyver, Connor W. Coley
Read original paper ↗ ·Machine intelligence for chemical reaction space
Wiley Interdisciplinary Reviews Computational Molecular Science · 2022-03-07
Philippe Schwaller, Alain C. Vaucher, Rubén Laplaza, Charlotte Bunne, Andreas Krause, Clémence Corminbœuf, Teodoro Laino
Read original paper ↗ ·Machine Learning May Sometimes Simply Capture Literature Popularity Trends: A Case Study of Heterocyclic Suzuki–Miyaura Coupling
Journal of the American Chemical Society · 2022-03-08
Wiktor Beker, Rafał Roszak, Agnieszka Wołos, Nicholas H. Angello, Vandana Rathore, Martin D. Burke, Bartosz A. Grzybowski
Read original paper ↗ ·Target high‐efficiency ionic liquids to promote H2SO4 ‐catalyzed C4 alkylation by machine learning
AIChE Journal · 2022-03-23
Weizhong Zheng, Zhihong Ma, Weizhen Sun, Ling Zhao
Read original paper ↗ ·Machine Learning for Chemical Reactivity: The Importance of Failed Experiments
Angewandte Chemie International Edition · 2022-05-05
Felix Strieth‐Kalthoff, Frederik Sandfort, Marius Kühnemund, Felix R. Schäfer, Herbert Kuchen, Frank Glorius
Read original paper ↗ ·Maschinelles Lernen zur Vorhersage chemischer Reaktivität: Die Bedeutung “gescheiterter” Experimente
Angewandte Chemie · 2022-05-05
Felix Strieth‐Kalthoff, Frederik Sandfort, Marius Kühnemund, Felix R. Schäfer, Herbert Kuchen, Frank Glorius
Read original paper ↗ ·Evaluation guidelines for machine learning tools in the chemical sciences
Nature Reviews Chemistry · 2022-05-24
Andreas Bender, Nadine Schneider, Marwin Segler, W. Patrick Walters, Ola Engkvist, Tiago Rodrigues
Read original paper ↗ ·Computational Catalyst Design with Data–Driven Tools – General Approaches and Applications
Elsevier eBooks · 2022-06-11
Jolene P. Reid
Read original paper ↗ ·Network search algorithms and scoring functions for advanced‐level computerized synthesis planning
Wiley Interdisciplinary Reviews Computational Molecular Science · 2022-06-16
Bartosz A. Grzybowski, Tomasz Badowski, Karol Molga, Sara Szymkuć
Read original paper ↗ ·Mechanistic Inference from Statistical Models at Different Data-Size Regimes
ACS Catalysis · 2022-06-17
Danilo Machado Lustosa, Anat Milo
Read original paper ↗ ·Molecular represention in AI-assisted synthesis chemistry: from sequence to graph
Scientia Sinica Chimica · 2022-07-11
Hongshuai Wang, Lujie Jin, Jie Feng, Zhihao Dong, Wei Deng, Youyong Li
Read original paper ↗ ·When machine learning meets molecular synthesis
Trends in Chemistry · 2022-08-31
João C. A. Oliveira, Johanna Frey, Shuo‐Qing Zhang, Li‐Cheng Xu, Xin Li, Shuwen Li, Xin Hong, Lutz Ackermann
Read original paper ↗ ·From theory to experiment: transformer-based generation enables rapid discovery of novel reactions
Journal of Cheminformatics · 2022-09-02
Xinqiao Wang, Chuansheng Yao, Yun Zhang, Jiahui Yu, Haoran Qiao, Chengyun Zhang, Yejian Wu, Renren Bai, Hongliang Duan
Read original paper ↗ ·Machine learning prediction of empirical polarity using SMILES encoding of organic solvents
Molecular Diversity · 2022-11-05
Vaneet Saini
Read original paper ↗ ·Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery
Chemical Science · 2022-11-28
Zhengkai Tu, Thijs Stuyver, Connor W. Coley
Read original paper ↗ ·Machine learning reaction barriers in low data regimes: a horizontal and diagonal transfer learning approach
Digital Discovery · 2023-01-01
Samuel G. Espley, Elliot H. E. Farrar, David Buttar, Simone Tomasi, Matthew N. Grayson
Read original paper ↗ ·On the use of real-world datasets for reaction yield prediction
Chemical Science · 2023-01-01
Mandana Saebi, Bozhao Nan, John E. Herr, Jessica Wahlers, Zhichun Guo, Andrzej M. Żurański, Thierry Kogej, Per‐Ola Norrby, Abigail G. Doyle, Nitesh V. Chawla, Olaf Wiest
Read original paper ↗ ·Predicting ruthenium catalysed hydrogenation of esters using machine learning
Digital Discovery · 2023-01-01
Challenger Mishra, Niklas von Wolff, Abhinav Tripathi, Claire N. Brodie, Neil David Lawrence, Aditya Ravuri, Éric A. G. Brémond, Annika Preiss, Amit Kumar
Read original paper ↗ ·Synthesize in a Smart Way: A Brief Introduction to Intelligence and Automation in Organic Synthesis
Challenges and advances in computational chemistry and physics · 2023-01-01
Dian‐Zhao Lin, Guichun Fang, Kuangbiao Liao
Read original paper ↗ ·The challenge of balancing model sensitivity and robustness in predicting yields: a benchmarking study of amide coupling reactions
Chemical Science · 2023-01-01
Zhen Liu, Yurii S. Moroz, Olexandr Isayev
Read original paper ↗ ·ConvPred : A deep learning‐based framework for predictions of potential organic reactions
AIChE Journal · 2023-01-10
Wenlong Wang, Qilei Liu, Yachao Dong, Jian Du, Qingwei Meng, Lei Zhang
Read original paper ↗ ·Machine‐Learning Classification for the Prediction of Catalytic Activity of Organic Photosensitizers in the Nickel(II)‐Salt‐Induced Synthesis of Phenols
Angewandte Chemie International Edition · 2023-01-16
Naoki Noto, Akira Yada, Takeshi Yanai, Susumu Saito
Read original paper ↗ ·Machine‐Learning Classification for the Prediction of Catalytic Activity of Organic Photosensitizers in the Nickel(II)‐Salt‐Induced Synthesis of Phenols
Angewandte Chemie · 2023-01-16
Naoki Noto, Akira Yada, Takeshi Yanai, Susumu Saito
Read original paper ↗ ·Benchmark of general-purpose machine learning-based quantum mechanical method AIQM1 on reaction barrier heights
The Journal of Chemical Physics · 2023-01-30
Yuxinxin Chen, Yanchi Ou, Peikun Zheng, Yaohuang Huang, Fuchun Ge, Pavlo O. Dral
Read original paper ↗ ·Molecular Machine Learning for Chemical Catalysis: Prospects and Challenges
Accounts of Chemical Research · 2023-01-30
Sukriti Singh, Raghavan B. Sunoj
Read original paper ↗ ·Machine Learning‐Guided Computational Screening of New Candidate Reactions with High Bioorthogonal Click Potential
Chemistry - A European Journal · 2023-02-14
Thijs Stuyver, Connor W. Coley
Read original paper ↗ ·Thermodynamics Evaluation of Selective Hydride Reduction for α,β-Unsaturated Carbonyl Compounds
Molecules · 2023-03-22
Baolong Chen, Sha Jing, Xiao‐Qing Zhu
Read original paper ↗ ·Computer-Aided Synthesis Planning for Biocatalysis
Elsevier eBooks · 2023-05-17
William Finnigan
Read original paper ↗ ·Machine Learning Strategies for Reaction Development: Toward the Low-Data Limit
Journal of Chemical Information and Modeling · 2023-06-14
Eunjae Shim, Ambuj Tewari, Tim Cernak, Paul M. Zimmerman
Read original paper ↗ ·Reaction performance prediction with an extrapolative and interpretable graph model based on chemical knowledge
Nature Communications · 2023-06-15
Shu-Wen Li, Li‐Cheng Xu, Cheng Zhang, Shuo‐Qing Zhang, Xin Hong
Read original paper ↗ ·Machine Learning Determination of New Hammett’s Constants for meta - and para -Substituted Benzoic Acid Derivatives Employing Quantum Chemical Atomic Charge Methods
The Journal of Organic Chemistry · 2023-07-11
Gabriel Monteiro‐de‐Castro, Júlio César Duarte, Itamar Borges
Read original paper ↗ ·Electrocatalyzed direct arene alkenylations without directing groups for selective late-stage drug diversification
Nature Communications · 2023-07-15
Zhipeng Lin, Uttam Dhawa, Xiaoyan Hou, Max Surke, Binbin Yuan, Shu-Wen Li, Yan‐Cheng Liou, Magnus J. Johansson, Li‐Cheng Xu, Chen-Hang Chao, Xin Hong, Lutz Ackermann
Read original paper ↗ ·Mechanisms and Stereoselectivities in the NHC-Catalyzed [4 + 2] Annulation of 2-Bromoenal and 6-Methyluracil-5-carbaldehyde
The Journal of Organic Chemistry · 2023-08-29
Yan Li, Mingchao Zhang, Zhiqiang Zhang
Read original paper ↗ ·Toward Universal Substituent Constants: Relating QTAIM Functional Group Descriptors to Substituent Effect Proxies
Journal of Chemical Information and Modeling · 2023-09-20
Kevin M. Lefrancois‐Gagnon, Robert C. Mawhinney
Read original paper ↗ ·Prediction of arsenic adsorption onto metal organic frameworks and adsorption mechanisms interpretation by machine learning
Journal of Environmental Management · 2023-10-04
Ting Xiong, Jiawen Cui, Zemin Hou, Xingzhong Yuan, Hou Wang, Jie Chen, Yi Yang, Yishi Huang, Xintao Xu, Changqing Su, Lijian Leng
Read original paper ↗ ·Comment on ‘Physics-based representations for machine learning properties of chemical reactions’
Machine Learning Science and Technology · 2023-10-06
Kevin Spiekermann, Thijs Stuyver, Lagnajit Pattanaik, William H. Green
Read original paper ↗ ·