Article Dans Une Revue Computers and Graphics Année : 2025

SHREC 2025: Protein surface shape retrieval including electrostatic potential

Yuki Kagaya
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  • PersonId : 1583174
Ruiwen He
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  • PersonId : 1583180
Haotian Li
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Chunyan Li
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Alireza Tehrani
Fanwang Meng
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  • PersonId : 1583182
Farnaz Heidar-Zadeh
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  • PersonId : 1583183

Résumé

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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hal-05263222 , version 1 (16-09-2025)

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Taher Yacoub, Camille Depenveiller, Atsushi Tatsuma, Tin Barisin, Eugen Rusakov, et al.. SHREC 2025: Protein surface shape retrieval including electrostatic potential. Computers and Graphics, 2025, 132, pp.104394. ⟨10.1016/j.cag.2025.104394⟩. ⟨hal-05263222⟩
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