GraphBNC：机器学习辅助预测金属纳米团簇与血液蛋白质之间的相互作用（Adv. Mater.）

IF 27.4 1区材料科学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Advanced Materials Pub Date : 2024-11-25 DOI:10.1002/adma.202470379

Antti Pihlajamäki, María Francisca Matus, Sami Malola, Hannu Häkkinen

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引用次数: 0

摘要

纳米生物界面的机器学习

本文章由计算机程序翻译，如有差异，请以英文原文为准。

GraphBNC: Machine Learning-Aided Prediction of Interactions Between Metal Nanoclusters and Blood Proteins (Adv. Mater. 47/2024)

查看原文本刊更多论文

GraphBNC: Machine Learning-Aided Prediction of Interactions Between Metal Nanoclusters and Blood Proteins (Adv. Mater. 47/2024)

Machine Learning for Nano-Bio Interfaces

In article number 2407046, Antti Pihlajamäki, María Francisca Matus, Sami Malola, and Hannu Häkkinen report a method based on graph theory and neural networks (GraphBNC) to predict atom-scale interactions between ligand-stabilized gold nanoclusters and proteins. The method uses dynamic data from molecular dynamics simulations, making GraphBNC particularly useful when empirical evidence is scarce.

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来源期刊

Advanced Materials 工程技术-材料科学：综合

CiteScore

43.00

自引率

4.10%

发文量

2182

审稿时长

2 months

期刊介绍： Advanced Materials, one of the world's most prestigious journals and the foundation of the Advanced portfolio, is the home of choice for best-in-class materials science for more than 30 years. Following this fast-growing and interdisciplinary field, we are considering and publishing the most important discoveries on any and all materials from materials scientists, chemists, physicists, engineers as well as health and life scientists and bringing you the latest results and trends in modern materials-related research every week.