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RESEARCH PAPER · Chemical Science · 2020

One class classification as a practical approach for accelerating π–π co-crystal discovery

Aikaterini Vriza, Angelos B. Canaj, Rebecca Vismara, Laurence J. Kershaw Cook, Troy D. Manning, Michael W. Gaultois, P.A. Wood, Vitaliy A. Kurlin, Neil G. Berry, Matthew Stephen Dyer, Matthew J. Rosseinsky

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42 direct citing works

One class classification as a practical approach for accelerating π–π co-crystal discoveryOriginalAccelerating the discovery of energetic melt-castable materials by a high-throughput virtual screening and experimental approach73High-throughput virtual screening for organic electronics: a comparative study of alternative strategies68SMINBR: An Integrated Network and Chemoinformatics Tool Specialized for Prediction of Two-Component Crystal Formation12Coupling complementary strategy to flexible graph neural network for quick discovery of coformer in diverse co-crystal materials117Molecular set transformer: attending to the co-crystals in the Cambridge structural database29Sustainable silver nanoparticles as the vector for green therapeutics in oncology3Synthesis of the C70 Fragment Buckybowl, Homosumanene, and Heterahomosumanenes via Ring-Expansion Reactions from Sumanenone21Chemometric-assisted cocrystallization: supervised pattern recognition for predicting the formation of new functional cocrystals17Prediction and design of cyclodextrin inclusion complexes formation via machine learning-based strategies26One class classification for the detection of β2 adrenergic receptor agonists using single-ligand dynamic interaction data4Cocrystal virtual screening based on the XGBoost machine learning model63From Concept to Synthesis: Developing Heat-Resistant High Explosives through Automated High-Throughput Virtual Screening14Accelerating metal–organic framework discovery via synthesisability prediction: the MFD evaluation method for one-class classification models3Engineering Single Component Luminogens to Multicomponent Charge‐transfer Co‐crystal Substrate as New Frontiers for Sensitive SERS Detection7What has scripting ever done for us? The CSD Python application programming interface (API)33Going beyond the Ordered Bulk: A Perspective on the Use of the Cambridge Structural Database for Predictive Materials Design10The development of classification-based machine-learning models for the toxicity assessment of chemicals associated with plastic packaging18High-throughput encapsulated nanodroplet screening for accelerated co-crystal discovery5Unlocking the Potential of Machine Learning in Co‐crystal Prediction by a Novel Approach Integrating Molecular Thermodynamics16Unlocking the Potential of Machine Learning in Co‐crystal Prediction by a Novel Approach Integrating Molecular Thermodynamics1Progress of Machine Learning in Molecular Crystal Design and Crystallization Development11Cocrystal engineering for constructing emissive ambipolar semiconductor based on diphenylanthracene and diperfluorophenylanthracene5Data-Driven Discovery of Polar Organic Cocrystals: Integration of Machine Learning and Automated Screening10Modulation of the Mechanical and Emission Properties of Cyano-Substituted Anthracene by Introduction of a Methylene Group2Interrogating the synthetic likelihood of metal–organic frameworks: a digital discovery perspective4Toxicity of polymers0Rational High-Throughput Screening of Cocrystals: An Explainable Deep Learning Framework Based on Morgan Fingerprints and Cross-Attention0Machine learning strategies integrating knowledge transfer and a cascaded framework: Guiding the discovery of NIR-II organic cocrystals0

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  1. Accelerating the discovery of energetic melt-castable materials by a high-throughput virtual screening and experimental approach

    Journal of Materials Chemistry A · 2021-01-01

    Siwei Song, Fang Chen, Yi Wang, Kangcai Wang, Mi Yan, Qinghua Zhang

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  2. High-throughput virtual screening for organic electronics: a comparative study of alternative strategies

    Journal of Materials Chemistry C · 2021-01-01

    Ömer H. Omar, Marcos del Cueto, Tahereh Nematiaram, Alessandro Troisi

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  3. SMINBR: An Integrated Network and Chemoinformatics Tool Specialized for Prediction of Two-Component Crystal Formation

    Journal of Chemical Information and Modeling · 2021-08-26

    Lulu Zheng, Bin Zhu, Zengrui Wu, Fang Liang, Minghuang Hong, Guixia Liu, Weihua Li, Guo‐Bin Ren, Yun Tang

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  4. Coupling complementary strategy to flexible graph neural network for quick discovery of coformer in diverse co-crystal materials

    Nature Communications · 2021-10-12

    Yuanyuan Jiang, Zongwei Yang, Jiali Guo, Hongzhen Li, Yijing Liu, Yanzhi Guo, Menglong Li, Xuemei Pu

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  5. Molecular set transformer: attending to the co-crystals in the Cambridge structural database

    Digital Discovery · 2022-01-01

    Aikaterini Vriza, Ioana Şovago, Daniel E. Widdowson, Vitaliy A. Kurlin, P.A. Wood, Matthew Stephen Dyer

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  6. Sustainable silver nanoparticles as the vector for green therapeutics in oncology

    Applied Nanoscience · 2022-02-07

    Dayum Iqbal, Amjad Hassan, Arsalan Ahmed Ansari, Nawshad Muhammad, Muhammad Adnan Khan, Saira Khalid, Faiza Sharif

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  7. Synthesis of the C70 Fragment Buckybowl, Homosumanene, and Heterahomosumanenes via Ring-Expansion Reactions from Sumanenone

    The Journal of Organic Chemistry · 2022-02-18

    Mikey Nishimoto, Yuta Uetake, Yumi Yakiyama, Fumitaka Ishiwari, Akinori Saeki, Hidehiro Sakurai

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  8. Chemometric-assisted cocrystallization: supervised pattern recognition for predicting the formation of new functional cocrystals

    Chemometrics and Intelligent Laboratory Systems · 2022-05-16

    Fabio Fornari, Fabio Montisci, Federica Bianchi, Marina Cocchi, Claudia Carraro, Francesca Cavaliere, Pietro Cozzini, Francesca Peccati, Paolo P. Mazzeo, Nicolò Riboni, Maria Careri, Alessia Bacchi

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  9. Prediction and design of cyclodextrin inclusion complexes formation via machine learning-based strategies

    Chemical Engineering Science · 2022-07-23

    Yiming Ma, Yue Niu, Huaiyu Yang, Jiayu Dai, Jiawei Lin, Huiqi Wang, Songgu Wu, Qiuxiang Yin, Ling Zhou, Junbo Gong

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  10. One class classification for the detection of β2 adrenergic receptor agonists using single-ligand dynamic interaction data

    Journal of Cheminformatics · 2022-10-29

    Luca Chiesa, Esther Kellenberger

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  11. Cocrystal virtual screening based on the XGBoost machine learning model

    Chinese Chemical Letters · 2022-10-31

    Dezhi Yang, Li Wang, Penghui Yuan, Qi An, Bin Su, Mingchao Yu, Ting Chen, Kun Hu, Li Zhang, Yang Lu, Guanhua Du

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  12. From Concept to Synthesis: Developing Heat-Resistant High Explosives through Automated High-Throughput Virtual Screening

    The Journal of Physical Chemistry C · 2023-09-14

    Zujia Lu, Yong Hu, Wen‐Shuai Dong, Wenli Cao, Tingwei Wang, Jianguo Zhang, Qiyao Yu

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  13. Accelerating metal–organic framework discovery via synthesisability prediction: the MFD evaluation method for one-class classification models

    Digital Discovery · 2024-01-01

    Chi Zhang, Dmytro Antypov, Matthew J. Rosseinsky, Matthew Stephen Dyer

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  14. Engineering Single Component Luminogens to Multicomponent Charge‐transfer Co‐crystal Substrate as New Frontiers for Sensitive SERS Detection

    Advanced Optical Materials · 2024-07-02

    Debika Barman, Debasish Barman, Kalishankar Bhattacharyya, Parameswar Krishnan Iyer

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  15. What has scripting ever done for us? The CSD Python application programming interface (API)

    Journal of Applied Crystallography · 2024-07-15

    Richard A. Sykes, Natalie T. Johnson, Christopher J. Kingsbury, Jürgen Harter, Andrew G. P. Maloney, Isaac J. Sugden, Suzanna Clare Ward, Ian J. Bruno, Stewart A. Adcock, P.A. Wood, Patrick McCabe, Alexandru A. Moldovan, Francis L. Atkinson, Ilenia Giangreco, Jason C. Cole

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  16. Going beyond the Ordered Bulk: A Perspective on the Use of the Cambridge Structural Database for Predictive Materials Design

    Crystal Growth & Design · 2024-08-19

    Ioanna Pallikara, Jonathan Michael Skelton, Lauren E. Hatcher, Anuradha R. Pallipurath

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  17. The development of classification-based machine-learning models for the toxicity assessment of chemicals associated with plastic packaging

    Journal of Hazardous Materials · 2024-11-30

    Md Mobarak Hossain, Kunal Roy

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  18. High-throughput encapsulated nanodroplet screening for accelerated co-crystal discovery

    Chemical Science · 2025-01-01

    Jessica P. Metherall, Philip Corner, James Francis McCabe, Michael Richard Probert, Michael J. Hall

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  19. Unlocking the Potential of Machine Learning in Co‐crystal Prediction by a Novel Approach Integrating Molecular Thermodynamics

    Angewandte Chemie International Edition · 2025-03-12

    Yutong Song, Yewei Ding, Junyi Su, Jian Li, Yuanhui Ji

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  20. Unlocking the Potential of Machine Learning in Co‐crystal Prediction by a Novel Approach Integrating Molecular Thermodynamics

    Angewandte Chemie · 2025-03-12

    Yutong Song, Yewei Ding, Junyi Su, Jian Li, Yuanhui Ji

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  21. Progress of Machine Learning in Molecular Crystal Design and Crystallization Development

    Engineering · 2025-06-04

    Shengzhe Jia, Yiming Ma, Yuechao Cao, Zhenguo Gao, Sohrab Rohani, Junbo Gong, Jingkang Wang

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  22. Cocrystal engineering for constructing emissive ambipolar semiconductor based on diphenylanthracene and diperfluorophenylanthracene

    Chinese Chemical Letters · 2025-07-20

    Liangliang Chen, Han Huang, Qin Zhu, Yiyun Zeng, Zhichun Shangguan, Jin Chen, Xunchang Wang, Cheng Li, Guanxin Zhang, Hongbing Fu, Deqing Zhang

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  23. Data-Driven Discovery of Polar Organic Cocrystals: Integration of Machine Learning and Automated Screening

    Journal of the American Chemical Society · 2025-10-30

    Huiyu Liu, Muyao Yu, Zikai Xie, Xiangyu Zhang, Yiru Sun, Yuyang Ling, Zezhao Xu, Zidi Wang, Na Yu, Yue‐Biao Zhang, Ping Huai, Qisheng Wang, Xuming He, Linjiang Chen, Weihong Zhu, Shan Hao Jiang

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  24. Modulation of the Mechanical and Emission Properties of Cyano-Substituted Anthracene by Introduction of a Methylene Group

    Crystal Growth & Design · 2025-11-22

    Tomohiro Seki, Masahiro Ikeda

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  25. Interrogating the synthetic likelihood of metal–organic frameworks: a digital discovery perspective

    Chemical Science · 2026-01-01

    Xiaoyu Wu, Jianwen Jiang

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  26. Toxicity of polymers

    Elsevier eBooks · 2026-01-01

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  27. Rational High-Throughput Screening of Cocrystals: An Explainable Deep Learning Framework Based on Morgan Fingerprints and Cross-Attention

    ACS Physical Chemistry Au · 2026-06-26

    Yukun Liu, Yukun Liu, Yu Liu, Yu Liu, Ziang Du, Haochun Lan, Hao Zheng, D J Yang, Yang Gao

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  28. Machine learning strategies integrating knowledge transfer and a cascaded framework: Guiding the discovery of NIR-II organic cocrystals

    Chemical Engineering Journal · 2026-09-16

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