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RESEARCH PAPER · Nature Communications · 2025

Towards global reaction feasibility and robustness prediction with high throughput data and bayesian deep learning

Haowen Zhong, Yilan Liu, Haibin Sun, Yuru Liu, Rentao Zhang, Baochen Li, Yi Yang, Yuqing Huang, Fei Yang, Frankie S. Mak, Klement Foo, Sen Lin, Tianshu Yu, Peng Wang, Xiaoxue Wang

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18 direct citing papers in this saved network. Explore the next layer with second-order citations.

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42 direct citing works

Towards global reaction feasibility and robustness prediction with high throughput data and bayesian deep learningOriginalComputational tools for the prediction of site- and regioselectivity of organic reactions33Applying Active Learning toward Building a Generalizable Model for Ni-Photoredox Cross-Electrophile Coupling of Aryl and Alkyl Bromides18Artificial intelligence for fibrous network design and mechanics7Synergy of Machine Learning and High‐Throughput Experimentation: A Road Toward Autonomous Synthesis8Predicting Reaction Feasibility and Selectivity of Aromatic C─H Thianthrenation with a QM–ML Hybrid Approach6Predicting Reaction Feasibility and Selectivity of Aromatic C─H Thianthrenation with a QM–ML Hybrid Approach1The Data-Centric Paradigm in Synthetic Chemistry: AI Reaction Modeling Needs More than Reactant-Product Pairs4Robust Chemical Reaction Condition Recommendations via Label Mix Strategy3A comprehensive survey of AI-based retrosynthesis planning: Datasets, models, and tools3An overview of reaction outcome prediction with physics-based and data-driven methods2Protocol-guided evaluation of reaction yield prediction under predictable distribution shifts: a review0Hardness Prediction of MoNbTaW Alloy Films Based on Machine Learning and Interpretability Analysis1Automated DFT-Machine Learning Integration Enables Data-Efficient and Generalizable Feasibility Predictions in Metallaphotoredox sp 2 –sp 3 Cross-Coupling Reactions2Artificial Intelligence Across the Drug Development Lifecycle2A chemically-aware validation framework for benchmarking large language models in materials synthesis planning1Encapsulated droplet array for intensified micromole-scale high-throughput reaction screening0ChemRANG: Building AI-ready datasets via mechanism-informed curation to learn reaction feasibility boundaries0AI-Predicted Full-Spectrum UV Correction Factors: An Analyte-Standard-Free Framework for Reaction Yield Quantification0

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18 direct citing papers saved.

  1. Computational tools for the prediction of site- and regioselectivity of organic reactions

    Chemical Science · 2025-01-01

    Lukas M. Sigmund, Michele Assante, Magnus J. Johansson, Per‐Ola Norrby, Kjell Jorner, Mikhail Kabeshov

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  2. Applying Active Learning toward Building a Generalizable Model for Ni-Photoredox Cross-Electrophile Coupling of Aryl and Alkyl Bromides

    Journal of the American Chemical Society · 2025-05-22

    Lucas W. Souza, Nathan D. Ricke, Braden C. Chaffin, Michael William-Patrick Fortunato, Shutian Jiang, Cihan Soylu, Thomas Caya, Sii Hong Lau, Katherine A. Wieser, Abigail G. Doyle, Kian L. Tan

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  3. Artificial intelligence for fibrous network design and mechanics

    AI for Science · 2025-06-01

    Yunhao Yang, Leitao Cao, Jing Ren, Wenli Gao, Shengjie Ling

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  4. Synergy of Machine Learning and High‐Throughput Experimentation: A Road Toward Autonomous Synthesis

    Chemistry - An Asian Journal · 2025-08-28

    Rizvi Syed Aal E Ali, Jiaolong Meng, Xuefeng Jiang

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  5. Predicting Reaction Feasibility and Selectivity of Aromatic C─H Thianthrenation with a QM–ML Hybrid Approach

    Angewandte Chemie International Edition · 2025-09-14

    Lukas M. Sigmund, Tina Seifert, Riya Halder, Giulia Bergonzini, Magnus J. Johansson, Per‐Ola Norrby, Kjell Jorner, Mikhail Kabeshov

    Read original paper ↗ ·

  6. Predicting Reaction Feasibility and Selectivity of Aromatic C─H Thianthrenation with a QM–ML Hybrid Approach

    Angewandte Chemie · 2025-09-14

    Lukas M. Sigmund, Tina Seifert, Riya Halder, Giulia Bergonzini, Magnus J. Johansson, Per‐Ola Norrby, Kjell Jorner, Mikhail Kabeshov

    Read original paper ↗ ·

  7. The Data-Centric Paradigm in Synthetic Chemistry: AI Reaction Modeling Needs More than Reactant-Product Pairs

    Communications in Computational Chemistry · 2025-10-06

    Mingjun Yang, Peiyu Zhang

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  8. Robust Chemical Reaction Condition Recommendations via Label Mix Strategy

    Journal of Chemical Information and Modeling · 2025-11-21

    Xin Yan, Haowen Zhong, Xiaoxue Wang

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  9. A comprehensive survey of AI-based retrosynthesis planning: Datasets, models, and tools

    The Innovation Informatics · 2026-01-01

    Peng-Cheng Zhao, Hong-Ze Zhou, Qiong Wang, Zhen-Yi Wu, Hui Yu, J. W. Shi

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  10. An overview of reaction outcome prediction with physics-based and data-driven methods

    Chemical Society Reviews · 2026-01-01

    Joonyoung F. Joung, Nicholas Casetti, Priyanka Raghavan, Connor W. Coley

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  11. Protocol-guided evaluation of reaction yield prediction under predictable distribution shifts: a review

    Reaction Chemistry & Engineering · 2026-01-01

    Zhengtong Qiu, Haoyu Yuan, Ao Chen, Chaonan Mu

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  12. Hardness Prediction of MoNbTaW Alloy Films Based on Machine Learning and Interpretability Analysis

    Materials · 2026-01-29

    Yanhan Yang, Tian-You Zhu, Wei Ren, W.L. Wang

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  13. Automated DFT-Machine Learning Integration Enables Data-Efficient and Generalizable Feasibility Predictions in Metallaphotoredox sp 2 –sp 3 Cross-Coupling Reactions

    ACS Catalysis · 2026-02-10

    Michele Assante, Sara Pahlén, Brenda Pijper, Lili Cao, Ole Specht, Nadja A. Simeth, Alexei A. Lapkin, Clive Green, Ola Engkvist, Giulia Bergonzini, Mikhail Kabeshov

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  14. Artificial Intelligence Across the Drug Development Lifecycle

    Medical Sciences · 2026-05-10

    Grigory A. Demyashkin, Паршенков Михаил Алексеевич, Sergey Zyryanov, Alexander Yavorskiy, Peter Shegai, Andrey Kaprin

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  15. A chemically-aware validation framework for benchmarking large language models in materials synthesis planning

    Journal of Cheminformatics · 2026-05-24

    Aobo Zhang

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  16. Encapsulated droplet array for intensified micromole-scale high-throughput reaction screening

    Nature Communications · 2026-06-05

    Jie Chen, Huijie Chen, Wei Qu, Junjie Huang, Yajun Tan, Haowen Zhong, Rentao Zhang, Yilan Liu, Qiang Zhang, Hanyu Gao, Xiaoxue Wang, Peng Wang, Yiming Mo

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  17. ChemRANG: Building AI-ready datasets via mechanism-informed curation to learn reaction feasibility boundaries

    Chinese Chemical Letters · 2026-07-01

    Pengxiang Cai, Zihao Ye, Shuzhou Sun, Longhan Zhang, Zhehong Ai, Yuqiang Li

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  18. AI-Predicted Full-Spectrum UV Correction Factors: An Analyte-Standard-Free Framework for Reaction Yield Quantification

    JACS Au · 2026-09-15

    Jiuchuang Yuan, Mingjun Yang, Yan Fan, Jing Guo, Lin Zhang, Guosheng Dou, Minjun Liu, Lu Tan, Liwen Fang, Guanfeng Yang, Qun Zeng, Jun Yan, Xuekun Shi, Sarah L. J. Trice, Jian Ma, Shuhao Wen, Christopher J. Welch, Peiyu Zhang

    Read original paper ↗ ·
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