Machine Learning Prediction of Flavonoid Cocrystal Formation Combined with Experimental Validation

Yue, Hong; Wang, Jingtao

doi:10.1021/acs.iecr.3c02556

Ind. Eng. Chem. Res.

2023

DOI: 10.1021/acs.iecr.3c02556

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Machine Learning Prediction of Flavonoid Cocrystal Formation Combined with Experimental Validation

Hong Yue,

Jingtao Wang

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Cited by 2 publications

References 71 publications

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Hesperetin–4,4′-bipyridine cocrystal: Polymorphism, crystal structures, and thermodynamic relationship

Zhu,

Wang,

et al. 2025

Journal of Molecular Structure

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Hesperetin–4,4′-bipyridine cocrystal: Polymorphism, crystal structures, and thermodynamic relationship

Zhu,

Wang,

et al. 2025

Journal of Molecular Structure

View full text Add to dashboard Cite

Prioritizing Computational Cocrystal Prediction Methods for Experimental Researchers: A Review to Find Efficient, Cost-Effective, and User-Friendly Approaches

Lemli,

Pál,

Salem

et al. 2024

IJMS

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Pharmaceutical cocrystals offer a versatile approach to enhancing the properties of drug compounds, making them an important tool in drug formulation and development by improving the therapeutic performance and patient experience of pharmaceutical products. The prediction of cocrystals involves using computational and theoretical methods to identify potential cocrystal formers and understand the interactions between the active pharmaceutical ingredient and coformers. This process aims to predict whether two or more molecules can form a stable cocrystal structure before performing experimental synthesis, thus saving time and resources. In this review, the commonly used cocrystal prediction methods are first overviewed and then evaluated based on three criteria: efficiency, cost-effectiveness, and user-friendliness. Based on these considerations, we suggest to experimental researchers without strong computational experiences which methods and tools should be tested as a first step in the workflow of rational design of cocrystals. However, the optimal choice depends on specific needs and resources, and combining methods from different categories can be a more powerful approach.

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Machine Learning Prediction of Flavonoid Cocrystal Formation Combined with Experimental Validation

Cited by 2 publications

References 71 publications

Hesperetin–4,4′-bipyridine cocrystal: Polymorphism, crystal structures, and thermodynamic relationship

Hesperetin–4,4′-bipyridine cocrystal: Polymorphism, crystal structures, and thermodynamic relationship

Prioritizing Computational Cocrystal Prediction Methods for Experimental Researchers: A Review to Find Efficient, Cost-Effective, and User-Friendly Approaches

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