浙江大学和中国科学院研究团队开创性地提出一种名为EquiScore的新型评分方法，巧妙融合人工智能与物理先验知识，成功登上国际顶级学术期刊Nature的子刊。该研究领域取得了引人注目的进展。
该团队致力于蛋白质-配体相互作用的研究，为深入理解生命活动过程中的关键反应机制提供了新的视角。此次提出的EquiScore方法巧妙地运用了异构图神经网络，整合了物理先验知识，并在等变几何空间中详细表征了蛋白质与配体之间的微妙互动。这一创新技术的引入不仅大大提高了蛋白质-配体相互作用评估的准确性，也预示着未来药物研发领域可能发生的革新。
该研究成果标志着我国在人工智能与生物医学领域的深度融合方面取得了重要突破，对于推动相关领域科技进步、提高我国在全球科研领域的竞争力具有重大意义。
英语如下：
News Title: “AI Integrates Physical Prior Knowledge: ZJU and Chinese Academy of Sciences Innovate Protein-Ligand Scoring Method in Nature Sub-Journal”
Keywords: Based on the above information, I have summarized three concise and brief news bulletins:
News Content: A research team from Zhejiang University and the Chinese Academy of Sciences has made a groundbreaking discovery by proposing a new scoring method called EquiScore. This innovative method seamlessly integrates artificial intelligence with physical prior knowledge, and has been successfully published in a sub-journal of the international top-tier academic journal Nature. This area of research has made remarkable progress.
The team focuses on the study of protein-ligand interactions, providing a new perspective for understanding the key reaction mechanisms in biological activities. The proposed EquiScore method巧妙地 utilizes a heterogeneous graph neural network, integrates physical prior knowledge, and extensively characterizes the delicate interactions between proteins and ligands in an equivariant geometric space. The introduction of this innovative technology not only greatly improves the accuracy of protein-ligand interaction assessments, but also suggests possible innovations in the field of drug development in the future.
This research achievement signifies an important breakthrough in the deep integration of artificial intelligence and biomedicine in China, and is of great significance for promoting technological advancements in related fields and enhancing China’s competitiveness in global scientific research.
【来源】https://www.jiqizhixin.com/articles/2024-06-13-8
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