{"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/accelerating-inference-in-molecular-diffusion","title":"Accelerating Inference in Molecular Diffusion Models with Latent Representations of Protein Structure","arxiv_id":"2311.13466","date":"2023-11-22","proceeding":null,"authors":["Ian Dunn","David Ryan Koes"],"abstract":"Diffusion generative models have emerged as a powerful framework for addressing problems in structural biology and structure-based drug design. These models operate directly on 3D molecular structures. Due to the unfavorable scaling of graph neural networks (GNNs) with graph size as well as the relatively slow inference speeds inherent to diffusion models, many existing molecular diffusion models rely on coarse-grained representations of protein structure to make training and inference feasible. However, such coarse-grained representations discard essential information for modeling molecular interactions and impair the quality of generated structures. In this work, we present a novel GNN-based architecture for learning latent representations of molecular structure. When trained end-to-end with a diffusion model for de novo ligand design, our model achieves comparable performance to one with an all-atom protein representation while exhibiting a 3-fold reduction in inference time.","url_abs":"https://arxiv.org/abs/2311.13466v2","url_pdf":"https://arxiv.org/pdf/2311.13466v2.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":"accelerating-inference-in-molecular-diffusion","repo_url":"https://github.com/dunni3/keypoint-diffusion","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"drug-design","task_name":"Drug Design"}],"methods":[{"method_slug":"diffusion","method_name":"Diffusion"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2311.13466","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.13466"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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/dunni3/keypoint-diffusion","reach":null}],"summary":{"ran_honours":1,"unverified":3},"by_repo_kind":{"official":{"samples":4,"ran":1,"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":"826fe75952e9c8fb","entry":"cosine_beta_schedule","repo":"dunni3/keypoint-diffusion","repo_kind":"official","path":"models/ligand_diffuser.py","file_url":"https://github.com/dunni3/keypoint-diffusion/blob/HEAD/models/ligand_diffuser.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"826fe75952e9c8fb"}},{"code_sha256_prefix":"2eb9e464e505772e","entry":"clip_noise_schedule","repo":"dunni3/keypoint-diffusion","repo_kind":"official","path":"models/ligand_diffuser.py","file_url":"https://github.com/dunni3/keypoint-diffusion/blob/HEAD/models/ligand_diffuser.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"2eb9e464e505772e"}},{"code_sha256_prefix":"08972b1cff29c116","entry":"element_fixer","repo":"dunni3/keypoint-diffusion","repo_kind":"official","path":"byop.py","file_url":"https://github.com/dunni3/keypoint-diffusion/blob/HEAD/byop.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"08972b1cff29c116"}},{"code_sha256_prefix":"97b0062845d63629","entry":"polynomial_schedule","repo":"dunni3/keypoint-diffusion","repo_kind":"official","path":"models/ligand_diffuser.py","file_url":"https://github.com/dunni3/keypoint-diffusion/blob/HEAD/models/ligand_diffuser.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"97b0062845d63629"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}