{"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/learning-multimodal-graph-to-graph-1","title":"Learning Multimodal Graph-to-Graph Translation for Molecular Optimization","arxiv_id":"1812.01070","date":"2018-12-03","proceeding":null,"authors":["Wengong Jin","Kevin Yang","Regina Barzilay","Tommi Jaakkola"],"abstract":"We view molecular optimization as a graph-to-graph translation problem. The\ngoal is to learn to map from one molecular graph to another with better\nproperties based on an available corpus of paired molecules. Since molecules\ncan be optimized in different ways, there are multiple viable translations for\neach input graph. A key challenge is therefore to model diverse translation\noutputs. Our primary contributions include a junction tree encoder-decoder for\nlearning diverse graph translations along with a novel adversarial training\nmethod for aligning distributions of molecules. Diverse output distributions in\nour model are explicitly realized by low-dimensional latent vectors that\nmodulate the translation process. We evaluate our model on multiple molecular\noptimization tasks and show that our model outperforms previous\nstate-of-the-art baselines.","url_abs":"http://arxiv.org/abs/1812.01070v3","url_pdf":"http://arxiv.org/pdf/1812.01070v3.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":"learning-multimodal-graph-to-graph-1","repo_url":"https://github.com/wengong-jin/iclr19-graph2graph","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"learning-multimodal-graph-to-graph-1","repo_url":"https://github.com/cbilodeau2/g2g_optimization","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"learning-multimodal-graph-to-graph-1","repo_url":"https://github.com/kovanostra/Message-passing-neural-network","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"learning-multimodal-graph-to-graph-1","repo_url":"https://github.com/kovanostra/graph-to-graph","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"learning-multimodal-graph-to-graph-1","repo_url":"https://github.com/kovanostra/message-passing-nn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"graph-to-graph-translation","task_name":"Graph-To-Graph Translation"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1812.01070","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.01070"}},"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/kovanostra/Message-passing-neural-network","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/kovanostra/graph-to-graph","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/cbilodeau2/g2g_optimization","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/wengong-jin/iclr19-graph2graph","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/kovanostra/message-passing-nn","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":2},"by_repo_kind":{"official":{"samples":2,"ran":0,"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":"ce9a23f3afe1e874","entry":"atom_features","repo":"wengong-jin/iclr19-graph2graph","repo_kind":"official","path":"fast_jtnn/jtmpn.py","file_url":"https://github.com/wengong-jin/iclr19-graph2graph/blob/HEAD/fast_jtnn/jtmpn.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"ce9a23f3afe1e874"}},{"code_sha256_prefix":"c0968545f446bb6a","entry":"onek_encoding_unk","repo":"wengong-jin/iclr19-graph2graph","repo_kind":"official","path":"fast_jtnn/jtmpn.py","file_url":"https://github.com/wengong-jin/iclr19-graph2graph/blob/HEAD/fast_jtnn/jtmpn.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"c0968545f446bb6a"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}