Mechanochemical co-crystallization: Insights and predictions

Gröls, Jan; Castro-Dominguez, Bernardo

doi:10.1016/j.compchemeng.2021.107416

Cited by 11 publications

(12 citation statements)

References 37 publications

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“…70 Previously, we highlighted that mechanochemical cocrystallization benefits from a high negative charge on the hydrogen-acceptor molecule as well as a high degree of polarization for both components. 29 Like co-crystallization, coamorphization also depends on the presence of isostructural molecules; nonetheless, mechanochemical co-amorphization is driven by electronic excitation and intramolecular immobility. Indeed, both co-amorphization and co-crystallization need intermolecular mixing and the formation of solid dispersions.…”

Section: Discussionmentioning

confidence: 99%

“…Molecular descriptors are commonly used in quantitative structure−property relationships and have been successfully implemented in the representation of small organic molecules during co-crystallization. 29 Molecular descriptors were calculated using the Mordred descriptor database (see Figure 2a). 39 A virtual library was created with all molecules and descriptors, producing 1033 features per molecule.…”

Section: Materials and Modeling Methodsmentioning

confidence: 99%

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Intelligent Mechanochemical Design of Co-Amorphous Mixtures

Gröls

Castro-Dominguez

2022

Crystal Growth & Design

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Mechanochemistry is a green preparation method that uses mechanical forces to prompt chemical reactions. This technique has shown its potential as an efficient alternative for several solvent-based processes (e.g., synthesis of co-crystals, metal complexes, or polymers); however, predicting its reactivity remains a challenge. In this study, a machine learning model was developed to gain insights into this process and predict the formation of co-amorphous mixtures. Co-amorphous mixtures are produced when the molecular arrangement of a crystalline active pharmaceutical ingredient is disrupted and maintained at "random" by the synergistic presence of a secondary structure. Co-amorphous mixtures can be designed as multicomponent drugs and often display an enhanced solubility and bioavailability. In this work, we generated a database of 418 inhouse amorphization experiments, novel to current literature, and informed data analysis (i.e., gradient boosting and neural networks) for predictive purposes and to extrapolate fundamental insights. By using 2066 chemical descriptors to train a gradient boost model, a predictive accuracy of >73% was achieved. This model was further used to predict and synthesize six novel co-amorphous mixtures. We expect that this novel database and the predictive model will aid at designing novel pharmaceuticals and advancing sustainable solvent-free processes.

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Section: Discussionmentioning

confidence: 99%

Section: Materials and Modeling Methodsmentioning

confidence: 99%

Intelligent Mechanochemical Design of Co-Amorphous Mixtures

Gröls

Castro-Dominguez

2022

Crystal Growth & Design

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“…This dataset published by [14] offers 1000 co-crystallisation events and records their outcome as determined by powder X-ray diffraction. Crystalline products were recorded as 1 and amorphous products recorded as 0.…”

Section: Co-crystal Propensity Via Mechanochemistrymentioning

confidence: 99%

“…All data used in this work were open-source and can be found alongside the original publications [14,36,37].…”

Section: Author Contributionsmentioning

confidence: 99%

“…Reasons for this include ready availability from open-source software packages and their growing historical record of publication. Examples that focus on the solid form engineering applications relevant to the case study presented in this work include crystallisation propensity, CC propensity, solubility, and even amorphous properties such as glass forming ability [10][11][12][13][14][15][16]. Descriptors represent tangible molecular properties that a user can readily translate into practical terms which can be controlled experimentally to affect the application's target [17].…”

Section: Introductionmentioning

confidence: 99%

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Images of chemical structures as molecular representations for deep learning

Wilkinson

Martinez-Hernandez

Wilson

et al. 2022

Journal of Materials Research

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Implementing Artificial Intelligence for chemical applications provides a wealth of opportunity for materials discovery, healthcare and smart manufacturing. For such applications to be successful, it is necessary to translate the properties of molecules into a digital format so they can be passed to the algorithms used for smart modelling. The literature has shown a wealth of different strategies for this task, yet there remains a host of limitations. To overcome these challenges, we present two-dimensional images of chemical structures as molecular representations. This methodology was evaluated against other techniques in both classification and regression tasks. Images unlocked (1) superior augmentation strategies, (2) application of specialist network architectures and (3) transfer learning, all contributing to superior performance and without prior specialised knowledge on cheminformatics required. This work takes advantage of image feature maps which do not rely on chemical properties and so can represent multi-component systems without further property calculations. Graphical abstract

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Mechanochemistry: Fundamental Principles and Applications

Dong,

Li,

Chen

et al. 2024

Advanced Science

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Mechanochemistry is an emerging research field at the interface of physics, mechanics, materials science, and chemistry. Complementary to traditional activation methods in chemistry, such as heat, electricity, and light, mechanochemistry focuses on the activation of chemical reactions by directly or indirectly applying mechanical forces. It has evolved as a powerful tool for controlling chemical reactions in solid state systems, sensing and responding to stresses in polymer materials, regulating interfacial adhesions, and stimulating biological processes. By combining theoretical approaches, simulations and experimental techniques, researchers have gained intricate insights into the mechanisms underlying mechanochemistry. In this review, the physical chemistry principles underpinning mechanochemistry are elucidated and a comprehensive overview of recent significant achievements in the discovery of mechanically responsive chemical processes is provided, with a particular emphasis on their applications in materials science. Additionally, The perspectives and insights into potential future directions for this exciting research field are offered.

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Mechanochemical co-crystallization: Insights and predictions

Cited by 11 publications

References 37 publications

Intelligent Mechanochemical Design of Co-Amorphous Mixtures

Intelligent Mechanochemical Design of Co-Amorphous Mixtures

Images of chemical structures as molecular representations for deep learning

Mechanochemistry: Fundamental Principles and Applications

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