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RESEARCH PAPER · CrystEngComm · 2018

Assessment of machine learning approaches for predicting the crystallization propensity of active pharmaceutical ingredients

Ayana Ghosh, Lydie Louis, Kapildev K. Arora, Bruno C. Hancock, Joseph F. Krzyzaniak, Paul A. Meenan, Serge Nakhmanson, Geoffrey P. F. Wood

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42 direct citing works

Assessment of machine learning approaches for predicting the crystallization propensity of active pharmaceutical ingredientsOriginalIs it usual to be unusual? An investigation into molecular conformations in organic crystals16Concerning Elusive Crystal Forms: The Case of Paracetamol80Recent Advances in Co-processed APIs and Proposals for Enabling Commercialization of These Transformative Technologies67Machine learning study of magnetism in uranium-based compounds23One class classification as a practical approach for accelerating π–π co-crystal discovery28Harnessing machine learning for development of microbiome therapeutics93Applications of Machine Learning in Solid Oral Dosage Form Development72Artificial neural networks in tandem with molecular descriptors as predictive tools for continuous liposome manufacturing39Molecular, Solid-State and Surface Structures of the Conformational Polymorphic Forms of Ritonavir in Relation to their Physicochemical Properties47Mechanochemical co-crystallization: Insights and predictions22Bridging microscopy with molecular dynamics and quantum simulations: An AtomAI based pipeline21Machine learning-based solubility prediction and methodology evaluation of active pharmaceutical ingredients in industrial crystallization39Influence of additives on the polymorphic manipulation of organic fluorescent crystals and its mechanism10Predicting pharmaceutical crystal morphology using artificial intelligence14Sustainable separations in pharmaceutical manufacturing5Machine Learning-Driven, Sensor-Integrated Microfluidic Device for Monitoring and Control of Supersaturation for Automated Screening of Crystalline Materials16Progress on polymorph control in small organic molecules: Pharmaceutics as an example6Applications of Artificial Intelligence and Machine Learning Algorithms to Crystallization160Images of chemical structures as molecular representations for deep learning8Insights into Cation Ordering of Double Perovskite Oxides from Machine Learning and Causal Relations36Prediction of Mefenamic Acid Crystal Shape by Random Forest Classification19Pharmaceutical Properties – The Importance of Solid Form Selection1Artificial Intelligence and Wastewater Treatment: A Global Scientific Perspective through Text Mining38Discovery of structure–property relations for molecules via hypothesis-driven active learning over the chemical space9Identification of novel organic polar materials: A machine learning study with importance sampling4Towards physics-informed explainable machine learning and causal models for materials research34Predictive Design of Hybrid Improper Ferroelectric Double Perovskite Oxides5Designing workflows for materials characterization22Artificial Intelligence Assisted Pharmaceutical Crystallization26Going beyond the Ordered Bulk: A Perspective on the Use of the Cambridge Structural Database for Predictive Materials Design10Predicting Crystalline Material Properties with AI: Bridging Molecular to Particle Scales6Applied Artificial Intelligence in Materials Science and Material Design74Progress of Machine Learning in Molecular Crystal Design and Crystallization Development11Unveiling the Facilitation and Stabilization mechanisms of Oxygen-Rich coformers on cefmetazole sodium crystals2Active deep kernel learning of molecular properties from structural embeddings1Perspectives Towards AI and ML0Data-driven prediction of crystal growth kinetics in polymer-stabilized amorphous drugs0Artificial Intelligence in Organic Synthesis0

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  1. Is it usual to be unusual? An investigation into molecular conformations in organic crystals

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  2. Concerning Elusive Crystal Forms: The Case of Paracetamol

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  3. Recent Advances in Co-processed APIs and Proposals for Enabling Commercialization of These Transformative Technologies

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  4. Machine learning study of magnetism in uranium-based compounds

    Physical Review Materials · 2020-06-19

    Ayana Ghosh, F. Ronning, Serge Nakhmanson, Jian‐Xin Zhu

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

    Chemical Science · 2020-12-09

    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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  6. Harnessing machine learning for development of microbiome therapeutics

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    Laura E. McCoubrey, Moe Elbadawi, Mine Orlu, Simon Gaisford, Abdul W. Basit

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  7. Applications of Machine Learning in Solid Oral Dosage Form Development

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    Hao Lou, Bo Lian, Michael J. Hageman

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  8. Artificial neural networks in tandem with molecular descriptors as predictive tools for continuous liposome manufacturing

    International Journal of Pharmaceutics · 2021-05-18

    Sameera Sansare, Tibo Duran, Hossein Mohammadiarani, Manish Kumar Goyal, Gowtham Yenduri, António Pedro Costa, Xiaoming Xu, Thomas F. O’Connor, Diane J. Burgess, Bodhisattwa Chaudhuri

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  9. Molecular, Solid-State and Surface Structures of the Conformational Polymorphic Forms of Ritonavir in Relation to their Physicochemical Properties

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    Chang Wang, Ian Rosbottom, Thomas D. Turner, Sydney Laing, Andrew G. P. Maloney, Ahmad Y. Sheikh, Robert Docherty, Qiuxiang Yin, Kevin J. Roberts

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  10. Mechanochemical co-crystallization: Insights and predictions

    Computers & Chemical Engineering · 2021-06-27

    Jan Roland Gröls, Bernardo Castro Dominguez

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  11. Bridging microscopy with molecular dynamics and quantum simulations: An AtomAI based pipeline

    arXiv (Cornell University) · 2021-09-09

    Ayana Ghosh, Maxim A Ziatdinov, Ondrej Dyck, Bobby G. Sumpter, Sergei V. Kalinin

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  12. Machine learning-based solubility prediction and methodology evaluation of active pharmaceutical ingredients in industrial crystallization

    Frontiers of Chemical Science and Engineering · 2021-10-13

    Yiming Ma, Zhenguo Gao, Peng Shi, Mingyang Chen, Songgu Wu, Chao Yang, Jingkang Wang, Jingcai Cheng, Junbo Gong

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  13. Influence of additives on the polymorphic manipulation of organic fluorescent crystals and its mechanism

    CrystEngComm · 2021-12-20

    Xiunan Zhang, Jingkang Wang, Fei Yu, Xiaowei Cheng, Yunhui Hao, Yue Liu, Xin Huang, Ting Wang, Hongxun Hao

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  14. Predicting pharmaceutical crystal morphology using artificial intelligence

    CrystEngComm · 2022-01-01

    Matthew R. Wilkinson, Uriel Martínez-Hernández, Laura Huggon, Chick C. Wilson, Bernardo Castro Dominguez

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  15. Sustainable separations in pharmaceutical manufacturing

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  16. Machine Learning-Driven, Sensor-Integrated Microfluidic Device for Monitoring and Control of Supersaturation for Automated Screening of Crystalline Materials

    ACS Sensors · 2022-01-20

    Paria Coliaie, Aditya Prajapati, Rabia Ali, Akshay Korde, Manish S. Kelkar, Nandkishor K. Nere, Meenesh R. Singh

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  17. Progress on polymorph control in small organic molecules: Pharmaceutics as an example

    Chinese Science Bulletin (Chinese Version) · 2022-04-24

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  18. Applications of Artificial Intelligence and Machine Learning Algorithms to Crystallization

    Chemical Reviews · 2022-06-27

    Christos Xiouras, Fabio Cameli, Gustavo Lunardon Quilló, Michail E. Kavousanakis, Dionisios G. Vlachos, Georgios D. Stefanidis

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  19. Images of chemical structures as molecular representations for deep learning

    Journal of materials research/Pratt's guide to venture capital sources · 2022-07-07

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  20. Insights into Cation Ordering of Double Perovskite Oxides from Machine Learning and Causal Relations

    Chemistry of Materials · 2022-08-05

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  21. Prediction of Mefenamic Acid Crystal Shape by Random Forest Classification

    Pharmaceutical Research · 2022-12-01

    Siya Nakapraves, Monika Warzecha, Chantal L. Mustoe, Vijay K. Srirambhatla, Alastair James Florence

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  22. Pharmaceutical Properties – The Importance of Solid Form Selection

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  23. Artificial Intelligence and Wastewater Treatment: A Global Scientific Perspective through Text Mining

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  24. Discovery of structure–property relations for molecules via hypothesis-driven active learning over the chemical space

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  25. Identification of novel organic polar materials: A machine learning study with importance sampling

    APL Machine Learning · 2023-12-01

    Ayana Ghosh, Dennis Trujillo, Subhashis Hazarika, Elizabeth Schiesser, M. J. Swamynathan, Saurabh Ghosh, Jian‐Xin Zhu, Serge Nakhmanson

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  26. Towards physics-informed explainable machine learning and causal models for materials research

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  27. Predictive Design of Hybrid Improper Ferroelectric Double Perovskite Oxides

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  28. Designing workflows for materials characterization

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

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  34. Unveiling the Facilitation and Stabilization mechanisms of Oxygen-Rich coformers on cefmetazole sodium crystals

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  35. Active deep kernel learning of molecular properties from structural embeddings

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  36. Perspectives Towards AI and ML

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  37. Data-driven prediction of crystal growth kinetics in polymer-stabilized amorphous drugs

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  38. Artificial Intelligence in Organic Synthesis

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