Pushing the limits of the heat of detonation <i>via</i> the construction of polynitro bipyrazole

Dong, Yaqun; Li, Miao; Liu, Jing; Liu, Yuji; Huang, Wei; Shreeve, Jean’ne M.; Tang, Yongxing

doi:10.1039/d3mh01381b

Cited by 1 publication

(1 citation statement)

References 36 publications

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“…Figure B–E displays compounds with –NO 2 , −SO 2 R, −CN, −COOH and −COR groups, respectively, showing significant contributions of these groups to p K a values, even though these groups are not always adjacent to the atoms of the ionization sites in some compounds. These electron-withdrawing groups affect the distribution of the electron cloud in the compound, ,− which in turn affects the ability of the compound to lose or gain protons. On the other hand, it can be seen that the AttenGpKa model is capable of capturing the contributions of atoms far from the ionization sites, indicating that the model can effectively learn the global information on small molecules.…”

Section: Results and Discussionmentioning

confidence: 99%

AttenGpKa: A Universal Predictor of Solvation Acidity Using Graph Neural Network and Molecular Topology

An,

Liu,

Cai

et al. 2024

J. Chem. Inf. Model.

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Rapid and accurate calculation of acid dissociation constant (pK a ) is crucial for designing chemical synthesis routes, optimizing catalysts, and predicting chemical behavior. Despite recent progress in machine learning, predicting solvation acidity, especially in nonaqueous solvents, remains challenging due to limited experimental data. This challenge arises from treating experimental values in different solvents as distinct data domains and modeling them separately. In this work, we treat both the solutes and solvents equally from a perspective of molecular topology and propose a highly universal framework called AttenGpKa for predicting solvation acidity. AttenGpKa is trained using 26,522 experimental pK a values from 60 pure and mixed solvents in the iBonD database. As a result, our model can simultaneously predict the pK a values of a compound in various solvents, including pure water, pure nonaqueous, and mixed solvents. AttenGpKa achieves universality by using graph neural networks and attention mechanisms to learn complex effects within solute and solvent molecules. Furthermore, encodings of both solute and solvent molecules are adaptively fused to simulate the influence of the solvent on acid dissociation. AttenGpKa demonstrates robust generalization in extensive validations. The interpretability studies further indicate that our model has effectively learnt electronic and solvent effects. A free-to-use software is provided to facilitate the use of AttenGpKa for pK a prediction.

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Section: Results and Discussionmentioning

confidence: 99%

AttenGpKa: A Universal Predictor of Solvation Acidity Using Graph Neural Network and Molecular Topology

An,

Liu,

Cai

et al. 2024

J. Chem. Inf. Model.

View full text Add to dashboard Cite

show abstract

Pushing the limits of the heat of detonation via the construction of polynitro bipyrazole

Abstract: An energetic compound with a trinitromethyl group has been achieved via a novel strategy of nitration of an ethylene bridged compound and shows excellent heat of detonation and promising properties.

Cited by 1 publication

References 36 publications

AttenGpKa: A Universal Predictor of Solvation Acidity Using Graph Neural Network and Molecular Topology

AttenGpKa: A Universal Predictor of Solvation Acidity Using Graph Neural Network and Molecular Topology

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