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利用持久同质特征,通过图形神经网络准确地预测缺陷形成能量

Zhenyao Fang1, Qimin Yan1

  • 1Department of Physics, Northeastern University, Boston, Massachusetts 02115, United States.

Chemistry of materials : a publication of the American Chemical Society
|March 3, 2025
PubMed
概括

持久的同类特征增强了缺陷研究的机器学习模型. 这种拓方法提高了材料特性和缺陷相互作用的预测准确性,加速了材料发现.

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Conserved Binding Sites01:49

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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
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