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Despite the growing interest in large molecular pre-trained models that provide informative representations for downstream tasks, attempts for multimodal pre-training approaches on the molecule domain were limited. To address this, we present a novel multimodal molecular pre-trained model that incorporates the modalities of structure and biochemical properties, drawing inspiration from recent advances in multimodal learning techniques. Our proposed model pipeline of data handling and training objectives aligns the structure/property features in a common embedding space, which enables the model to regard bidirectional information between the molecules' structure and properties. These contributions emerge synergistic knowledge, allowing us to tackle both multimodal and unimodal downstream tasks through a single model. Through extensive experiments, we demonstrate that our model shows remarkable capabilities in solving various meaningful chemical challenges, including conditional molecule generation, property prediction, molecule classification, and reaction prediction.","url_abs":"https://arxiv.org/abs/2211.10590v4","url_pdf":"https://arxiv.org/pdf/2211.10590v4.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":"molecular-structure-property-co-trained","repo_url":"https://github.com/jinhojsk515/SPMM","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"molecular-property-prediction","task_name":"Molecular Property Prediction"},{"task_slug":"property-prediction","task_name":"Property Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/molecular-property-prediction-on-bace-1","task":"Molecular Property Prediction","dataset":"BACE","model":"SPMM","rank_in_archive_order":8,"of":20,"metrics":{"RMSE":"1.108","ROC-AUC":"83.0"},"uses_additional_data":false},{"leaderboard":"/sota/molecular-property-prediction-on-bbbp-1","task":"Molecular Property Prediction","dataset":"BBBP","model":"SPMM","rank_in_archive_order":14,"of":29,"metrics":{"ROC-AUC":"73.3"},"uses_additional_data":false},{"leaderboard":"/sota/molecular-property-prediction-on-clearance","task":"Molecular Property Prediction","dataset":"Clearance","model":"SPMM","rank_in_archive_order":1,"of":2,"metrics":{"RMSE":"44.752"},"uses_additional_data":false},{"leaderboard":"/sota/molecular-property-prediction-on-esol","task":"Molecular Property Prediction","dataset":"ESOL","model":"SPMM","rank_in_archive_order":15,"of":20,"metrics":{"RMSE":"0.810"},"uses_additional_data":false},{"leaderboard":"/sota/molecular-property-prediction-on-freesolv","task":"Molecular Property Prediction","dataset":"FreeSolv","model":"SPMM","rank_in_archive_order":15,"of":22,"metrics":{"RMSE":"1.859"},"uses_additional_data":false},{"leaderboard":"/sota/molecular-property-prediction-on","task":"Molecular Property Prediction","dataset":"Lipophilicity","model":"SPMM","rank_in_archive_order":5,"of":13,"metrics":{"RMSE":"0.706"},"uses_additional_data":false},{"leaderboard":"/sota/molecular-property-prediction-on-sider-1","task":"Molecular Property Prediction","dataset":"SIDER","model":"SPMM","rank_in_archive_order":11,"of":19,"metrics":{"ROC-AUC":"64.7"},"uses_additional_data":false},{"leaderboard":"/sota/molecular-property-prediction-on-clintox-1","task":"Molecular Property Prediction","dataset":"clintox","model":"SPMM","rank_in_archive_order":6,"of":20,"metrics":{"ROC-AUC":"91.0"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2211.10590","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.10590"}},"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. 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