@article{GAGLIARDI202220,
title = {SHREC 2022: Protein–ligand binding site recognition},
journal = {Computers & Graphics},
volume = {107},
pages = {20-31},
year = {2022},
issn = {0097-8493},
doi = {https://doi.org/10.1016/j.cag.2022.07.005},
url = {https://www.sciencedirect.com/science/article/pii/S0097849322001236},
author = {Luca Gagliardi and Andrea Raffo and Ulderico Fugacci and Silvia Biasotti and Walter Rocchia and Hao Huang and Boulbaba Ben Amor and Yi Fang and Yuanyuan Zhang and Xiao Wang and Charles Christoffer and Daisuke Kihara and Apostolos Axenopoulos and Stelios Mylonas and Petros Daras},
keywords = {SHREC, 3D segmentation, Computational biology, Molecular modeling, Binding site prediction},
abstract = {This paper presents the methods that have participated in the SHREC 2022 contest on protein-ligand binding site recognition. The prediction of protein-ligand binding regions is an active research domain in computational biophysics and structural biology and plays a relevant role for molecular docking and drug design. The goal of the contest is to assess the effectiveness of computational methods in recognizing ligand binding sites in a protein based on its geometrical structure. Performances of the segmentation algorithms are analyzed according to two evaluation scores describing the capacity of a putative pocket to contact a ligand and to pinpoint the correct binding region. Despite some methods perform remarkably, we show that simple non-machine-learning approaches remain very competitive against data-driven algorithms. In general, the task of pocket detection remains a challenging learning problem which suffers of intrinsic difficulties due to the lack of negative examples (data imbalance problem).}
}