{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/shrec-2022-protein-ligand-binding-site","title":"SHREC 2022: Protein-ligand binding site recognition","arxiv_id":"2206.06035","date":"2022-06-13","proceeding":null,"authors":["Luca Gagliardi","Andrea Raffo","Ulderico Fugacci","Silvia Biasotti","Walter Rocchia","Hao Huang","Boulbaba Ben Amor","Yi Fang","Yuanyuan Zhang","Xiao Wang","Charles Christoffer","Daisuke Kihara","Apostolos Axenopoulos","Stelios Mylonas","Petros Daras"],"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).","url_abs":"https://arxiv.org/abs/2206.06035v4","url_pdf":"https://arxiv.org/pdf/2206.06035v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"shrec-2022-protein-ligand-binding-site","repo_url":"https://github.com/concept-lab/shrec22_proteinligandbenchmark","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"shrec-2022-protein-ligand-binding-site","repo_url":"https://github.com/lucagl/moad_ligandfinder","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"shrec-2022-protein-ligand-binding-site","repo_url":"https://github.com/stemylonas/deepsurf_shrec22","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"AGPL-3.0"}}],"tasks":[{"task_slug":"drug-design","task_name":"Drug Design"},{"task_slug":"molecular-docking","task_name":"Molecular Docking"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2206.06035","atlas_url":"https://app.syntology.ai/?focus=2206.06035","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.06035"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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