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RESEARCH PAPER · Journal of Chemical Information and Modeling · 2024

Exploring Chemical Reaction Space with Machine Learning Models: Representation and Feature Perspective

Yuheng Ding, Bo Qiang, Qixuan Chen, Yiqiao Liu, Liangren Zhang, Zhenming Liu

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Exploring Chemical Reaction Space with Machine Learning Models: Representation and Feature PerspectiveOriginalRecent advances from computer-aided drug design to artificial intelligence drug design50Evolutionary features for task-specific machine-learning applications1Supervised machine learning in drug discovery and development: Algorithms, applications, challenges, and prospects103Anti‐inflammatory effects of natural products from vitamin C‐rich fruits17Machine learning-guided strategies for reaction conditions design and optimization49ArcaNN: automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials38Machine learning-assisted retrosynthesis planning: Current status and future prospects18Computation-guided exploration of the reaction parameter space of N , N -dimethylformamide hydrolysis6Molecular representation learning: cross-domain foundations and future Frontiers27Multi‐modal Homogeneous Chemical Reaction Performance Prediction with Graph and Chemical Language Information7Integrating a multitask graph neural network with DFT calculations for site-selectivity prediction of arenes and mechanistic knowledge generation19A meta-learning approach for selectivity prediction in asymmetric catalysis11General Chemically Intuitive Atom- and Bond-Level DFT Descriptors for Machine Learning Approaches to Reaction Condition Prediction7Predicting Reactivity with Machine Learning1CSMILES: A Compact, Human-Readable SMILES Extension for Conformations4Accelerating the discovery of high-efficiency donor–acceptor pairs in organic photovoltaics via SolarPCE-Net guided screening2Learning Multidimensional Electronegativity of Selected Atom Types in Organic Molecules Using Graph Neural Networks2Graph neural networks driven acceleration in drug discovery27Improving Reaction Yield Prediction with Chemical Atom-Level Reaction Learning1Radical-Net: A chemistry-enhanced transformer for elementary radical reactions in pollutant chemistry23Mechanism‐Oriented Reaction Descriptors for Dispersity Prediction in Nitroxide‐Mediated Radical Polymerization Considering Mediator Stability1Role of artificial intelligence in the chemical industry transition to a sustainable, circular, and net-zero future0DAPM:Dual Path Yield Prediction Model Integrating Anti-Noise Convolution and Position Compensation0Applications of AI in Organic Chemistry0Toward on-the-fly prediction of reaction energetics for high-throughput screening2Toward Rational Design of Reaction-Based Fluorescent Probes: A Reaction-Centric Perspective on Machine Learning in Chemical Sensing0

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26 direct citing papers saved.

  1. Recent advances from computer-aided drug design to artificial intelligence drug design

    RSC Medicinal Chemistry · 2024-01-01

    Keran Wang, Yanwen Huang, Yan Wang, Qidong You, Lei Wang

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  2. Evolutionary features for task-specific machine-learning applications

    Chem · 2024-06-01

    Scott Laverty, Sourav Dey, Andrew F. Zahrt

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  3. Supervised machine learning in drug discovery and development: Algorithms, applications, challenges, and prospects

    Machine Learning with Applications · 2024-07-24

    George Obaido, Ibomoiye Domor Mienye, Oluwaseun Francis Egbelowo, Ikiomoye Douglas Emmanuel, Adeola Ogunleye, Blessing Ogbuokiri, Pere Mienye, Kehinde Aruleba

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  4. Anti‐inflammatory effects of natural products from vitamin C‐rich fruits

    Food Frontiers · 2024-09-04

    Sioi Chan, Ping Xiong, Min Zhao, Siyuan Zhang, Rong-Bo Zheng, Jizeng Ye, Ka Iong Chan, Chuyuan Li, Zhangfeng Zhong

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  5. Machine learning-guided strategies for reaction conditions design and optimization

    Beilstein Journal of Organic Chemistry · 2024-10-04

    Lung-Yi Chen, Yi‐Pei Li

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  6. ArcaNN: automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials

    Digital Discovery · 2024-10-30

    Rolf David, Miguel de la Puente, Axel Gomez, Olaia Anton, Guillaume Stirnemann, Damien Laage

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  7. Machine learning-assisted retrosynthesis planning: Current status and future prospects

    Chinese Journal of Chemical Engineering · 2024-11-29

    Yixin Wei, Leyu Shan, Tong Qiu, Diannan Lu, Zheng Liu

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  8. Computation-guided exploration of the reaction parameter space of N , N -dimethylformamide hydrolysis

    Digital Discovery · 2025-01-01

    Ignas Pakamorė, Ross Stewart Forgan

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  9. Molecular representation learning: cross-domain foundations and future Frontiers

    Digital Discovery · 2025-01-01

    Rahul Sheshanarayana, Fengqi You

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  10. Multi‐modal Homogeneous Chemical Reaction Performance Prediction with Graph and Chemical Language Information

    Chinese Journal of Chemistry · 2025-02-20

    Shen Wang, Weiren Zhao, Yining Liu, Yang Li

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  11. Integrating a multitask graph neural network with DFT calculations for site-selectivity prediction of arenes and mechanistic knowledge generation

    Nature Synthesis · 2025-04-07

    Xinran Chen, Zijing Zhang, Xin Hong, Lutz Ackermann

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  12. A meta-learning approach for selectivity prediction in asymmetric catalysis

    Nature Communications · 2025-04-15

    Sukriti Singh, José Miguel Hernández-Lobato

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  13. General Chemically Intuitive Atom- and Bond-Level DFT Descriptors for Machine Learning Approaches to Reaction Condition Prediction

    Journal of Chemical Information and Modeling · 2025-06-17

    Miguel Nouman, Richard B. Canty, Brent A. Koscher, Matthew A. McDonald, Klavs F. Jensen

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  14. Predicting Reactivity with Machine Learning

    Unknown journal · 2025-06-23

    Lauriane Jacot‐Descombes, Kjell Jorner

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  15. CSMILES: A Compact, Human-Readable SMILES Extension for Conformations

    Journal of Chemical Information and Modeling · 2025-09-26

    James W. Furness, Kevin B. Moore, Arteum D. Bochevarov

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  16. Accelerating the discovery of high-efficiency donor–acceptor pairs in organic photovoltaics via SolarPCE-Net guided screening

    Journal of Materials Chemistry A · 2025-10-08

    Xingyu Liu, Bo Hu, Peng Liu, Meng Huang, Minghui Li, Y. Wan, Bram Hoex, Tong Xie

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  17. Learning Multidimensional Electronegativity of Selected Atom Types in Organic Molecules Using Graph Neural Networks

    ACS Omega · 2025-10-09

    Da Bean Han, Gyoung S. Na, Hyunwoo Kim

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  18. Graph neural networks driven acceleration in drug discovery

    Acta Pharmaceutica Sinica B · 2025-10-18

    Rui Wang, Chunlin Zhuang

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  19. Improving Reaction Yield Prediction with Chemical Atom-Level Reaction Learning

    Journal of Chemical Information and Modeling · 2025-10-29

    Yijingxiu Lu, Yinhua Piao, Ming Shen, Sun Kim

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  20. Radical-Net: A chemistry-enhanced transformer for elementary radical reactions in pollutant chemistry

    Journal of Hazardous Materials · 2025-11-27

    Zhi Tao Huang, Jiang Yu, Jiang Yu, Wei He, Peng Huang, Siwei Deng, Hongbin Jiang, Jie Yu, Jie Yu, Chun Yang, Cheng Han, Yinying Jiang, Weiwei Zhu, Yucheng Liu

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  21. Mechanism‐Oriented Reaction Descriptors for Dispersity Prediction in Nitroxide‐Mediated Radical Polymerization Considering Mediator Stability

    Journal of Polymer Science · 2026-01-09

    Tatsuya Mori, Shunsuke Mieda, Koharu Kodama, Tomoyuki Miyao

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  22. Role of artificial intelligence in the chemical industry transition to a sustainable, circular, and net-zero future

    Current Opinion in Chemical Engineering · 2026-02-19

    Avan Kumar, Sunghoon Kim, Bhavik R. Bakshi

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  23. DAPM:Dual Path Yield Prediction Model Integrating Anti-Noise Convolution and Position Compensation

    Unknown journal · 2026-04-01

    Su Hu, Huanjin Wen, Limin Wang, Fenglu Huang, Ting Zhou, Yongbo Xiao, Xuming Han

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  24. Applications of AI in Organic Chemistry

    ChemistrySelect · 2026-06-01

    Muhammad Khuzaifa, Irsa Kainat, Qingle Zeng

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  25. Toward on-the-fly prediction of reaction energetics for high-throughput screening

    Chemical Physics Reviews · 2026-06-01

    Jasper De Landsheere, Maximilian P.-P. Kovar, Konstantin Mark, Leon Ganser, Leonard Galustian, Johannes Karwounopoulos, Charlotte Gerhaher, Esther Heid

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  26. Toward Rational Design of Reaction-Based Fluorescent Probes: A Reaction-Centric Perspective on Machine Learning in Chemical Sensing

    ACS Applied Optical Materials · 2026-06-04

    Jintian Lyu, Jiamin Zhong, Nan Zhou, Kui Du

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