arXiv 2509.129762025-09-16cs.CVq-bio.BM

SHREC 2025：包含静电势的蛋白质表面形状检索

SHREC 2025: Protein surface shape retrieval including electrostatic potential

organization= Laboratoire GBCM, EA7528, Conservatoire Nationale des Arts et Métiers , city= Paris , country= France；organization= National Institute of Technology, Tsuyama College , city= Tsuyama , country= Japan；organization= CONTACT AI Research, CONTACT Software GmbH , city= Bremen , country= Germany；organization= Changsha University of Science；organization= Department of Biological Sciences, Purdue University , city= West Lafayette, IN , country= USA；organization= Department of Computer Science, Purdue University , city= West Lafayette, IN , country= USA；organization= De Vinci Higher Education, De Vinci Research Center , city= Paris , country= France；organization= IMT Nord Europe, Institut Mines-Télécom, Université de Lille, Center for Digital Systems , city= Lille , country= France；organization= Yunnan Normal University , city= Kunming, Yunnan , country= China；organization= New York University Abu Dhabi , city= Abu Dhabi , country= UAE；organization= Department of Chemistry, Queen’s University , city= Kingston, ON , country= Canada；organization= University of Science, VNU-HCM , city= Ho Chi Minh City , country= Vietnam；organization= Vietnam National University , city= Ho Chi Minh City , country= Vietnam

Taher Yacoub, Camille Depenveiller, Atsushi Tatsuma, Tin Barisin, Eugen Rusakov, Udo Gobel, Yuxu Peng, Shiqiang Deng, Yuki Kagaya, Joon Hong Park, Daisuke Kihar… 展开作者

Taher Yacoub, Camille Depenveiller, Atsushi Tatsuma, Tin Barisin, Eugen Rusakov, Udo Gobel, Yuxu Peng, Shiqiang Deng, Yuki Kagaya, Joon Hong Park, Daisuke Kihara, Marco Guerra, Giorgio Palmieri, Andrea Ranieri, Ulderico Fugacci, Silvia Biasotti, Ruiwen He, Halim Benhabiles, Adnane Cabani, Karim Hammoudi, Haotian Li, Hao Huang, Chunyan Li, Alireza Tehrani, Fanwang Meng, Farnaz Heidar-Zadeh, Tuan-Anh Yang, Matthieu Montes

备注：: Published in Computers & Graphics, Elsevier. 59 pages, 12 figures
发表信息：: Computers & Graphics Volume 132, November 2025, Article 104394
DOI：: 10.1016/j.cag.2025.104394

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AI总结：

本文介绍了 SHREC 2025 蛋白质表面形状检索赛道，9 支团队在 11,555 个蛋白质表面数据集上评估了 15 种方法，发现结合静电势互补性的方法取得了最佳检索性能，凸显了额外分子表面描述符的重要性。

摘要：

此次专用于蛋白质表面形状检索的 SHREC 2025 赛道共有 9 支团队参与。我们在包含 11,555 个蛋白质表面且已计算静电势（一种关键的分子表面描述符）的大型数据集上，评估了 15 种提出方法在检索上的性能。提出方法在检索上的性能通过不同指标（Accuracy、Balanced accuracy、F1 score、Precision 和 Recall）进行评估。最佳的检索性能由使用了与分子表面形状互补的静电势的提出方法所取得。这一观察结果对于数据有限的类别同样成立，凸显了考虑额外分子表面描述符的重要性。

英文摘要：

This SHREC 2025 track dedicated to protein surface shape retrieval involved 9 participating teams. We evaluated the performance in retrieval of 15 proposed methods on a large dataset of 11,555 protein surfaces with calculated electrostatic potential (a key molecular surface descriptor). The performance in retrieval of the proposed methods was evaluated through different metrics (Accuracy, Balanced accuracy, F1 score, Precision and Recall). The best retrieval performance was achieved by the proposed methods that used the electrostatic potential complementary to molecular surface shape. This observation was also valid for classes with limited data which highlights the importance of taking into account additional molecular surface descriptors.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。