{"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/gps-an-optimised-hybrid-mpnn-transformer-for","title":"GPS++: An Optimised Hybrid MPNN/Transformer for Molecular Property Prediction","arxiv_id":"2212.02229","date":"2022-11-18","proceeding":null,"authors":["Dominic Masters","Josef Dean","Kerstin Klaser","Zhiyi Li","Sam Maddrell-Mander","Adam Sanders","Hatem Helal","Deniz Beker","Ladislav Rampášek","Dominique Beaini"],"abstract":"This technical report presents GPS++, the first-place solution to the Open Graph Benchmark Large-Scale Challenge (OGB-LSC 2022) for the PCQM4Mv2 molecular property prediction task. Our approach implements several key principles from the prior literature. At its core our GPS++ method is a hybrid MPNN/Transformer model that incorporates 3D atom positions and an auxiliary denoising task. The effectiveness of GPS++ is demonstrated by achieving 0.0719 mean absolute error on the independent test-challenge PCQM4Mv2 split. Thanks to Graphcore IPU acceleration, GPS++ scales to deep architectures (16 layers), training at 3 minutes per epoch, and large ensemble (112 models), completing the final predictions in 1 hour 32 minutes, well under the 4 hour inference budget allocated. Our implementation is publicly available at: https://github.com/graphcore/ogb-lsc-pcqm4mv2.","url_abs":"https://arxiv.org/abs/2212.02229v2","url_pdf":"https://arxiv.org/pdf/2212.02229v2.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":"gps-an-optimised-hybrid-mpnn-transformer-for","repo_url":"https://github.com/graphcore/ogb-lsc-pcqm4mv2","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"molecular-property-prediction","task_name":"Molecular Property Prediction"},{"task_slug":"property-prediction","task_name":"Property Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2212.02229","atlas_url":"https://app.syntology.ai/?focus=2212.02229","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.02229"}},"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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/graphcore/ogb-lsc-pcqm4mv2","reach":null}],"summary":{"ran_fixture":2},"by_repo_kind":{"official":{"samples":2,"ran":2,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"13042a0d32a2135c","entry":"format_out_tensor","repo":"graphcore/ogb-lsc-pcqm4mv2","repo_kind":"official","path":"inference.py","file_url":"https://github.com/graphcore/ogb-lsc-pcqm4mv2/blob/HEAD/inference.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"13042a0d32a2135c"}},{"code_sha256_prefix":"c56cba19ef506982","entry":"format_predictions","repo":"graphcore/ogb-lsc-pcqm4mv2","repo_kind":"official","path":"inference.py","file_url":"https://github.com/graphcore/ogb-lsc-pcqm4mv2/blob/HEAD/inference.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"c56cba19ef506982"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}