Taher Yacoub, Camille Depenveiller, Atsushi Tatsuma, Tin Barisin, Eugen Rusakov, Udo Göbel, 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
{"title":"SHREC 2025：蛋白质表面形状检索包括静电电位。","authors":"Taher Yacoub, Camille Depenveiller, Atsushi Tatsuma, Tin Barisin, Eugen Rusakov, Udo Göbel, 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","doi":"","DOIUrl":null,"url":null,"abstract":"<p><p>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 11,565 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.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12458584/pdf/","citationCount":"0","resultStr":"{\"title\":\"SHREC 2025: Protein Surface Shape Retrieval including Electrostatic potential.\",\"authors\":\"Taher Yacoub, Camille Depenveiller, Atsushi Tatsuma, Tin Barisin, Eugen Rusakov, Udo Göbel, 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\",\"doi\":\"\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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 11,565 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.</p>\",\"PeriodicalId\":93888,\"journal\":{\"name\":\"ArXiv\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-09-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12458584/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ArXiv\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ArXiv","FirstCategoryId":"1085","ListUrlMain":"","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
SHREC 2025: Protein Surface Shape Retrieval including Electrostatic potential.
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 11,565 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.
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