{"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/mol-mamba-enhancing-molecular-representation","title":"MOL-Mamba: Enhancing Molecular Representation with Structural & Electronic Insights","arxiv_id":"2412.16483","date":"2024-12-21","proceeding":null,"authors":["Jingjing Hu","Dan Guo","Zhan Si","Deguang Liu","Yunfeng Diao","Jing Zhang","Jinxing Zhou","Meng Wang"],"abstract":"Molecular representation learning plays a crucial role in various downstream tasks, such as molecular property prediction and drug design. To accurately represent molecules, Graph Neural Networks (GNNs) and Graph Transformers (GTs) have shown potential in the realm of self-supervised pretraining. However, existing approaches often overlook the relationship between molecular structure and electronic information, as well as the internal semantic reasoning within molecules. This omission of fundamental chemical knowledge in graph semantics leads to incomplete molecular representations, missing the integration of structural and electronic data. To address these issues, we introduce MOL-Mamba, a framework that enhances molecular representation by combining structural and electronic insights. MOL-Mamba consists of an Atom & Fragment Mamba-Graph (MG) for hierarchical structural reasoning and a Mamba-Transformer (MT) fuser for integrating molecular structure and electronic correlation learning. Additionally, we propose a Structural Distribution Collaborative Training and E-semantic Fusion Training framework to further enhance molecular representation learning. Extensive experiments demonstrate that MOL-Mamba outperforms state-of-the-art baselines across eleven chemical-biological molecular datasets.","url_abs":"https://arxiv.org/abs/2412.16483v2","url_pdf":"https://arxiv.org/pdf/2412.16483v2.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":"mol-mamba-enhancing-molecular-representation","repo_url":"https://github.com/xian-sh/mol-mamba","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"drug-design","task_name":"Drug Design"},{"task_slug":"mamba","task_name":"Mamba"},{"task_slug":"molecular-property-prediction","task_name":"Molecular Property Prediction"},{"task_slug":"property-prediction","task_name":"Property Prediction"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"molecular-representation","task_name":"molecular representation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2412.16483","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.16483"}},"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/xian-sh/mol-mamba","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"ran_draft_wrong":1,"ran_violates":1,"unverified":2},"by_repo_kind":{"official":{"samples":4,"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":"18cf88504a3521e6","entry":"freq_cnt","repo":"xian-sh/mol-mamba","repo_kind":"official","path":"datasets/fragment/mol_bpe.py","file_url":"https://github.com/xian-sh/mol-mamba/blob/HEAD/datasets/fragment/mol_bpe.py","link_basis":"harvester_set","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"18cf88504a3521e6"}},{"code_sha256_prefix":"55f1419d4d8c483c","entry":"transpose","repo":"xian-sh/mol-mamba","repo_kind":"official","path":"models/loss_info_nce.py","file_url":"https://github.com/xian-sh/mol-mamba/blob/HEAD/models/loss_info_nce.py","link_basis":"harvester_set","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"55f1419d4d8c483c"}},{"code_sha256_prefix":"06ea4a05299d8d58","entry":"criterion","repo":"xian-sh/mol-mamba","repo_kind":"official","path":"models/mamba_fuser.py","file_url":"https://github.com/xian-sh/mol-mamba/blob/HEAD/models/mamba_fuser.py","link_basis":"harvester_set","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":"06ea4a05299d8d58"}},{"code_sha256_prefix":"54b175c46812e410","entry":"info_nce","repo":"xian-sh/mol-mamba","repo_kind":"official","path":"models/loss_info_nce.py","file_url":"https://github.com/xian-sh/mol-mamba/blob/HEAD/models/loss_info_nce.py","link_basis":"harvester_set","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":"54b175c46812e410"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}