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.
@article{arxiv.2509.12976,
title = {SHREC 2025: Protein surface shape retrieval including electrostatic potential},
author = {Taher Yacoub and Camille Depenveiller and Atsushi Tatsuma and Tin Barisin and Eugen Rusakov and Udo Gobel and Yuxu Peng and Shiqiang Deng and Yuki Kagaya and Joon Hong Park and Daisuke Kihara and Marco Guerra and Giorgio Palmieri and Andrea Ranieri and Ulderico Fugacci and Silvia Biasotti and Ruiwen He and Halim Benhabiles and Adnane Cabani and Karim Hammoudi and Haotian Li and Hao Huang and Chunyan Li and Alireza Tehrani and Fanwang Meng and Farnaz Heidar-Zadeh and Tuan-Anh Yang and Matthieu Montes},
journal= {arXiv preprint arXiv:2509.12976},
year = {2025}
}
Comments
Published in Computers & Graphics, Elsevier. 59 pages, 12 figures