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Updated: Jul 8, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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可转移的机器学习 原子间潜在的键解离能 药物样分子的能量预测

Elena Gelžinytė1, Mario Öeren2, Matthew D Segall2

  • 1Engineering Laboratory, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, U.K.

Journal of chemical theory and computation
|December 18, 2023
PubMed
概括

我们开发了一种可转移的MACE潜力用于类似药物的分子,准确地预测细胞染色体P450代谢中的基因物种的键解离能. 这种方法提高了对复杂反应现有潜力的准确性.

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