{"type":"ScholarlyArticle","title":"DO-CoLM: Dynamic 3D Conformation Relationships Capture with Self-Adaptive Ordering Molecular Relational Modeling in Language Models","authors":[{"name":"Zhuo Chen","slug":"zhuo-chen","given":"Zhuo","family":"Chen","affiliation":"University of Science and Technology of China (USTC) , Hefei , China; Suzhou Institute for Advanced Research , USTC , Suzhou , China"},{"name":"Jiahui Zhang","slug":"jiahui-zhang","given":"Jiahui","family":"Zhang","affiliation":"University of Science and Technology of China (USTC) , Hefei , China; Suzhou Institute for Advanced Research , USTC , Suzhou , China"},{"name":"Sihan Wang","slug":"sihan-wang","given":"Sihan","family":"Wang","affiliation":"University of Science and Technology of China (USTC) , Hefei , China; Suzhou Institute for Advanced Research , USTC , Suzhou , China"},{"name":"Hongxin Xiang","slug":"hongxin-xiang","given":"Hongxin","family":"Xiang","affiliation":"Hunan University , Hunan , China"},{"name":"Jianmin Wang","slug":"jianmin-wang","given":"Jianmin","family":"Wang","affiliation":"Yonsei University , Seoul , Korea"},{"name":"Wenjie Du","slug":"wenjie-du","given":"Wenjie","family":"Du","affiliation":"University of Science and Technology of China (USTC) , Hefei , China; Suzhou Institute for Advanced Research , USTC , Suzhou , China"},{"name":"Yang Wang","slug":"yang-wang","given":"Yang","family":"Wang","affiliation":"University of Science and Technology of China (USTC) , Hefei , China; Suzhou Institute for Advanced Research , USTC , Suzhou , China"}],"abstract":"Molecular Relational Learning (MRL) aims to understand interactions between molecular pairs, playing a critical role in advancing biochemical research. Recently, Large Language Models (LLMs), with their extensive knowledge bases and advanced reasoning capabilities, have emerged as powerful tools for MRL. However, existing LLMs, which primarily rely on SMILES strings and molecular graphs, face two major challenges. They struggle to capture molecular stereochemistry and dynamics, as molecules possess multiple 3D conformations with varying reactivity and dynamic transformation relationships that are essential for accurately predicting molecular interactions but cannot be effectively represented by 1D SMILES or 2D molecular graphs. Additionally, these models do not consider the autoregressive nature of LLMs, overlooking the impact of input order on model performance. To address these issues, we propose DO-CoLM: a Dynamic relationship capture and self-adaptive Ordering 3D molecular Conformation LM for MRL. By introducing modules to dynamically model intra-molecular and inter-molecular conformational relationships and adaptively adjust the molecular modality input order, DO-CoLM achieves superior performance, as demonstrated by experimental results on 12 cross-domain datasets.","doi":"10.24963/ijcai.2025/546","url":"https://ijcai.org/proceedings/2025/546","pdf":"https://ijcai.org/proceedings/2025/0546.pdf","markdown":"https://ijcai.org/proceedings/2025/546.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/546","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Machine Learning","track":"Main Track","number":546,"pages":{"first":4905,"last":4913},"keywords":["Natural Language Processing: NLP: Language generation","Machine Learning: ML: Multi-modal learning"],"videos":[],"references":[{"position":1,"raw":"[Alonso et al., 2006] Hernan Alonso, Andrey A Bliznyuk, and Jill E Gready. Combining docking and molecular dy- namic simulations in drug design. Medicinal research re- views, 26(5):531-568, 2006.","title":"Combining docking and molecular dynamic simulations in drug design","authors":"Hernan Alonso, Andrey A Bliznyuk, Jill E Gready","year":"2006","venue":"Medicinal research reviews","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Andrade et al., 2009] Carolina H Andrade, Kerly FM Pasqualoto, Elizabeth I Ferreira, and Anton J Hopfin- ger. Rational design and 3d-pharmacophore mapping of 5- thiourea-substituted α-thymidine analogues as mycobac- terial tmpk inhibitors. Journal of chemical information and modeling, 49(4):1070-1078, 2009.","title":"Rational design and 3d-pharmacophore mapping of 5thiourea-substituted α-thymidine analogues as mycobacterial tmpk inhibitors","authors":"Carolina H Andrade, F M Kerly, Elizabeth I Pasqualoto, Anton J Ferreira, Hopfinger","year":"2009","venue":"Journal of chemical information and modeling","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Berglund et al., 2023] Lukas Berglund, Meg Tong, Max Kaufmann, Mikita Balesni, Asa Cooper Stickland, Tomasz Korbak, and Owain Evans. The reversal curse: Llms trained on\" a is b\" fail to learn\" b is a\". arXiv preprint arXiv:2309.12288, 2023.","title":"The reversal curse: Llms trained on\" a is b\" fail to learn\" b is a","authors":"Lukas Berglund, Meg Tong, Max Kaufmann, Mikita Balesni, Asa Cooper Stickland, Tomasz Korbak, Owain Evans","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Christofidellis et al., 2023] Dimitrios Christofidellis, Gior- gio Giannone, Jannis Born, Ole Winther, Teodoro Laino, and Matteo Manica. Unifying molecular and textual rep- resentations via multi-task language modelling. arXiv preprint arXiv:2301.12586, 2023.","title":"Unifying molecular and textual representations via multi-task language modelling","authors":"Dimitrios Christofidellis, Giorgio Giannone, Jannis Born, Ole Winther, Teodoro Laino, Matteo Manica","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Cuturi, 2013] Marco Cuturi. Sinkhorn distances: Light- speed computation of optimal transport. Advances in neu- ral information processing systems, 26, 2013.","title":"Sinkhorn distances: Lightspeed computation of optimal transport","authors":"Marco Cuturi","year":"2013","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Deac et al., 2019] Andreea Deac, Yu-Hsiang Huang, Petar Veličković, Pietro Liò, and Jian Tang. Drug-drug adverse effect prediction with graph co-attention. arXiv preprint arXiv:1905.00534, 2019.","title":"Drug-drug adverse effect prediction with graph co-attention","authors":"Andreea Deac, Yu-Hsiang Huang, Petar Veličković, Pietro Liò, Jian Tang","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":7,"raw":"Du et al., ] Wenjie Du, Shuai Zhang, Zhaohui Cai, Zhiyuan Liu, Junfeng Fang, Jianmin Wang, and Yang Wang. Molecular merged hypergraph neural network for explain- able solvation free energy prediction. Research, 0(ja).","title":"Molecular merged hypergraph neural network for explainable solvation free energy prediction","authors":"Du","year":null,"venue":"Research","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Du et al., 2024] Wenjie Du, Shuai Zhang, Di Wu, Jun Xia, Ziyuan Zhao, Junfeng Fang, and Yang Wang. MMGNN: A molecular merged graph neural network for explainable solvation free energy prediction. In Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, IJCAI 2024, Jeju, South Korea, August 3-9, 2024, pages 5808-5816. ijcai.org, 2024.","title":"MMGNN: A molecular merged graph neural network for explainable solvation free energy prediction","authors":"Wenjie Du, Shuai Zhang, Di Wu, Jun Xia, Ziyuan Zhao, Junfeng Fang, Yang Wang","year":"2024","venue":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, IJCAI 2024","doi":null,"url":null,"internal_url":"/proceedings/2024/642"},{"position":9,"raw":"[Edwards et al., 2021] Carl Edwards, ChengXiang Zhai, and Heng Ji. Text2mol: Cross-modal molecule retrieval with natural language queries. In EMNLP (1), pages 595-607. Association for Computational Linguistics, 2021.","title":"Text2mol: Cross-modal molecule retrieval with natural language queries","authors":"Edwards","year":"2021","venue":"EMNLP (1)","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Edwards et al., 2022] Carl Edwards, Tuan Lai, Kevin Ros, Garrett Honke, Kyunghyun Cho, and Heng Ji. Translation between molecules and natural language. arXiv preprint arXiv:2204.11817, 2022.","title":"Translation between molecules and natural language","authors":"Carl Edwards, Tuan Lai, Kevin Ros, Garrett Honke, Kyunghyun Cho, Heng Ji","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Fang et al., 2022] Xiaomin Fang, Lihang Liu, Jieqiong Lei, Donglong He, Shanzhuo Zhang, Jingbo Zhou, Fan Wang, Hua Wu, and Haifeng Wang. Geometry-enhanced molecu- lar representation learning for property prediction. Nature Machine Intelligence, 4(2):127-134, 2022.","title":"Geometry-enhanced molecular representation learning for property prediction","authors":"Xiaomin Fang, Lihang Liu, Jieqiong Lei, Donglong He, Shanzhuo Zhang, Jingbo Zhou, Fan Wang, Hua Wu, Haifeng Wang","year":"2022","venue":"Nature Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Fang et al., 2024] Junfeng Fang, Shuai Zhang, Chang Wu, Zhengyi Yang, Zhiyuan Liu, Sihang Li, Kun Wang, Wen- jie Du, and Xiang Wang. Moltc: Towards molecular relational modeling in language models. arXiv preprint arXiv:2402.03781, 2024.","title":"Moltc: Towards molecular relational modeling in language models","authors":"Junfeng Fang, Shuai Zhang, Chang Wu, Zhengyi Yang, Zhiyuan Liu, Sihang Li, Kun Wang, Wenjie Du, Xiang Wang","year":"2024","venue":null,"doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Fu et al., 2020] Tianfan Fu, Cao Xiao, and Jimeng Sun. Core: Automatic molecule optimization using copy & re- fine strategy. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 638-645, 2020.","title":"Core: Automatic molecule optimization using copy & refine strategy","authors":"Fu","year":"2020","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Jablonka et al., 2024] Kevin Maik Jablonka, Philippe Schwaller, Andres Ortega-Guerrero, and Berend Smit. Leveraging large language models for predictive chem- istry. Nature Machine Intelligence, 6(2):161-169, 2024.","title":"Leveraging large language models for predictive chemistry","authors":"Kevin Maik, Jablonka, Philippe Schwaller, Andres Ortega-Guerrero, Berend Smit","year":"2024","venue":"Nature Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Lee et al., 2023] Namkyeong Lee, Dongmin Hyun, Gy- oung S. Na, Sungwon Kim, Junseok Lee, and Chanyoung Park. Conditional graph information bottleneck for molec- ular relational learning. In ICML, volume 202 of Proceed- ings of Machine Learning Research, pages 18852-18871.","title":"Conditional graph information bottleneck for molecular relational learning","authors":"Lee","year":"2023","venue":"Proceedings of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"PMLR, 2023.","title":null,"authors":"Pmlr","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Li et al., 2023] Zimeng Li, Shichao Zhu, Bin Shao, Xi- angxiang Zeng, Tong Wang, and Tie-Yan Liu. Dsn-ddi: an accurate and generalized framework for drug-drug in- teraction prediction by dual-view representation learning. Briefings in Bioinformatics, 24(1):bbac597, 2023.","title":"Dsn-ddi: an accurate and generalized framework for drug-drug interaction prediction by dual-view representation learning","authors":"Li","year":"2023","venue":"Briefings in Bioinformatics","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Li et al., 2024] Sihang Li, Zhiyuan Liu, Yanchen Luo, Xi- ang Wang, Xiangnan He, Kenji Kawaguchi, Tat-Seng Chua, and Qi Tian. 3d-molm: Towards 3d molecule-text interpretation in language models. In ICLR, 2024.","title":"3d-molm: Towards 3d molecule-text interpretation in language models","authors":"Sihang Li, Zhiyuan Liu, Yanchen Luo, Xiang Wang, Xiangnan He, Kenji Kawaguchi, Tat-Seng Chua, Qi Tian","year":"2024","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Liang et al., 2023] Youwei Liang, Ruiyi Zhang, Li Zhang, and Pengtao Xie. Drugchat: Towards enabling chatgpt- like capabilities on drug molecule graphs. ArXiv, abs/2309.03907, 2023.","title":"Drugchat: Towards enabling chatgptlike capabilities on drug molecule graphs","authors":"Youwei Liang, Ruiyi Zhang, Li Zhang, Pengtao Xie","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Lin et al., 2022] Shenggeng Lin, Yanjing Wang, Lingfeng Zhang, Yanyi Chu, Yatong Liu, Yitian Fang, Mingming Jiang, Qiankun Wang, Bowen Zhao, Yi Xiong, et al. Mdf- sa-ddi: predicting drug-drug interaction events based on multi-source drug fusion, multi-source feature fusion and transformer self-attention mechanism. Briefings in Bioin- formatics, 23(1):bbab421, 2022.","title":"Mdfsa-ddi: predicting drug-drug interaction events based on multi-source drug fusion, multi-source feature fusion and transformer self-attention mechanism","authors":"Lin","year":"2022","venue":"Briefings in Bioinformatics","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Liu et al., 2023] Zhiyuan Liu, Sihang Li, Yanchen Luo, Hao Fei, Yixin Cao, Kenji Kawaguchi, Xiang Wang, and Tat- Seng Chua. Molca: Molecular graph-language modeling with cross-modal projector and uni-modal adapter. arXiv preprint arXiv:2310.12798, 2023.","title":"Molca: Molecular graph-language modeling with cross-modal projector and uni-modal adapter","authors":"Zhiyuan Liu, Sihang Li, Yanchen Luo, Hao Fei, Yixin Cao, Kenji Kawaguchi, Xiang Wang, Tat-Seng Chua","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Meng et al., 2023] Fanwang Meng, Hanwen Zhang, Juan Samuel Collins-Ramirez, and Paul W. Ayers. Something for nothing: Improved solvation free energy prediction with learning. 2023.","title":"Something for nothing: Improved solvation free energy prediction with learning","authors":"Meng","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Morris et al., 2019] Christopher Morris, Martin Ritzert, Matthias Fey, William L Hamilton, Jan Eric Lenssen, Gau- rav Rattan, and Martin Grohe. Weisfeiler and leman go neural: Higher-order graph neural networks. In Proceed- ings of the AAAI conference on artificial intelligence, vol- ume 33, pages 4602-4609, 2019.","title":"Weisfeiler and leman go neural: Higher-order graph neural networks","authors":"Christopher Morris, Martin Ritzert, Matthias Fey, Jan William L Hamilton, Gaurav Eric Lenssen, Martin Rattan, Grohe","year":"2019","venue":"Proceedings of the AAAI conference on artificial intelligence","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Mortier et al., 2015] Jérémie Mortier, Christin Rakers, Mar- cel Bermudez, Manuela S Murgueitio, Sereina Riniker, and Gerhard Wolber. The impact of molecular dynam- ics on drug design: applications for the characterization of ligand-macromolecule complexes. Drug discovery today, 20(6):686-702, 2015.","title":"The impact of molecular dynamics on drug design: applications for the characterization of ligand-macromolecule complexes","authors":"Jérémie Mortier, Christin Rakers, Marcel Bermudez, Manuela S Murgueitio, Sereina Riniker, Gerhard Wolber","year":"2015","venue":"Drug discovery today","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Nyamabo et al., 2021] Arnold K Nyamabo, Hui Yu, and Jian-Yu Shi. Ssi-ddi: substructure-substructure interac- tions for drug-drug interaction prediction. Briefings in Bioinformatics, 22(6):bbab133, 2021.","title":"Ssi-ddi: substructure-substructure interactions for drug-drug interaction prediction","authors":"Hui Arnold K Nyamabo, Jian-Yu Yu, Shi","year":"2021","venue":"Briefings in Bioinformatics","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Qi et al., 2017] Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas. Pointnet: Deep learning on point sets for 3d classification and segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 652-660, 2017.","title":"Pointnet: Deep learning on point sets for 3d classification and segmentation","authors":"Hao Charles R Qi, Kaichun Su, Leonidas J Mo, Guibas","year":"2017","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Rong et al., 2020] Yu Rong, Yatao Bian, Tingyang Xu, Weiyang Xie, Ying Wei, Wenbing Huang, and Junzhou Huang. Self-supervised graph transformer on large-scale molecular data. In NeurIPS, 2020.","title":"Self-supervised graph transformer on large-scale molecular data","authors":"Yu Rong, Yatao Bian, Tingyang Xu, Weiyang Xie, Ying Wei, Wenbing Huang, Junzhou Huang","year":"2020","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Ryu et al., 2018] Jae Yong Ryu, Hyun Uk Kim, and Sang Yup Lee. Deep learning improves prediction of drug- drug and drug-food interactions. Proceedings of the na- tional academy of sciences, 115(18):E4304-E4311, 2018.","title":"Deep learning improves prediction of drugdrug and drug-food interactions","authors":"Jae Yong Ryu, Hyun Uk Kim, Sang Yup Lee","year":"2018","venue":"Proceedings of the national academy of sciences","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Sagawa and Kojima, 2023] Tatsuya Sagawa and Ryosuke Kojima. Reactiont5: a large-scale pre-trained model to- wards application of limited reaction data. arXiv preprint arXiv:2311.06708, 2023.","title":"Reactiont5: a large-scale pre-trained model towards application of limited reaction data","authors":"Kojima Sagawa, Tatsuya Sagawa, Ryosuke Kojima","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Sizhe Liu et al., 2024] Sizhe Sizhe Liu, Jun Xia, Lecheng Zhang, Yuchen Liu, Yue Liu, Wenjie Du, Zhangyang Gao, Bozhen Hu, Cheng Tan, Stan Z Li, et al. Flexmol: A flex- ible toolkit for benchmarking molecular relational learn- ing. Advances in Neural Information Processing Systems, 37:35454-35467, 2024.","title":"Flexmol: A flexible toolkit for benchmarking molecular relational learning","authors":"Sizhe Liu","year":"2024","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Skjærven et al., 2011] Lars Skjærven, Nathalie Reuter, and Aurora Martinez. Dynamics, flexibility and ligand- induced conformational changes in biological macro- molecules: a computational approach. Future Medicinal Chemistry, 3(16):2079-2100, 2011.","title":"Dynamics, flexibility and ligandinduced conformational changes in biological macromolecules: a computational approach","authors":"Skjærven","year":"2011","venue":"Future Medicinal Chemistry","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Tan et al., 2024] Zhijie Tan, Xu Chu, Weiping Li, and Tong Mo. Order matters: Exploring order sensitivity in multimodal large language models. arXiv preprint arXiv:2410.16983, 2024.","title":"Order matters: Exploring order sensitivity in multimodal large language models","authors":"Zhijie Tan, Xu Chu, Weiping Li, Tong Mo","year":"2024","venue":null,"doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Tang et al., 2024] Xiangru Tang, Andrew Tran, Jeffrey Tan, and Mark B. Gerstein. Mollm: A unified language model for integrating biomedical text with 2d and 3d molecular representations. bioRxiv, 2024.","title":"Mollm: A unified language model for integrating biomedical text with 2d and 3d molecular representations","authors":"Tang","year":"2024","venue":"bioRxiv","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Taylor et al., 2022a] Ross Taylor, Marcin Kardas, Guillem Cucurull, Thomas Scialom, Anthony Hartshorn, Elvis Sar- avia, Andrew Poulton, Viktor Kerkez, and Robert Stojnic. Galactica: A large language model for science. arXiv preprint arXiv:2211.09085, 2022.","title":"Galactica: A large language model for science","authors":"Taylor","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Taylor et al., 2022b] Ross Taylor, Marcin Kardas, Guillem Cucurull, Thomas Scialom, Anthony Hartshorn, Elvis Sar- avia, Andrew Poulton, Viktor Kerkez, and Robert Stojnic. Galactica: A large language model for science. arXiv preprint arXiv:2211.09085, 2022.","title":"Galactica: A large language model for science","authors":"Taylor","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Vermeire and Green, 2021] Florence H Vermeire and William H Green. Transfer learning for solvation free energies: From quantum chemistry to experiments. Chemical Engineering Journal, 418:129307, 2021.","title":"Transfer learning for solvation free energies: From quantum chemistry to experiments","authors":"Green ; Vermeire, H Florence, William H Vermeire, Green","year":"2021","venue":"Chemical Engineering Journal","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Vinyals et al., 2015] Oriol Vinyals, Samy Bengio, and Man- junath Kudlur. Order matters: Sequence to sequence for sets. arXiv preprint arXiv:1511.06391, 2015.","title":"Order matters: Sequence to sequence for sets","authors":"Oriol Vinyals, Samy Bengio, Manjunath Kudlur","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Wu et al., 2022] Fang Wu, Shuting Jin, Yinghui Jiang, Xu- rui Jin, Bowen Tang, Zhangming Niu, Xiangrong Liu, Qiang Zhang, Xiangxiang Zeng, and Stan Z Li. Pre- training of equivariant graph matching networks with con- formation flexibility for drug binding. Advanced Science, 9(33):2203796, 2022.","title":"Pretraining of equivariant graph matching networks with conformation flexibility for drug binding","authors":"Fang Wu, Shuting Jin, Yinghui Jiang, Xurui Jin, Bowen Tang, Zhangming Niu, Xiangrong Liu, Qiang Zhang, Xiangxiang Zeng, Stan Z Li","year":"2022","venue":"Advanced Science","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Yu et al., 2023] Jiahui Yu, Chengwei Zhang, Yingying Cheng, Yun-Fang Yang, Yuan-Bin She, Fengfan Liu, Weike Su, and An Su. Solvbert for solvation free energy and solubility prediction: a demonstration of an nlp model for predicting the properties of molecular complexes. Dig- ital Discovery, 2(2):409-421, 2023.","title":"Solvbert for solvation free energy and solubility prediction: a demonstration of an nlp model for predicting the properties of molecular complexes","authors":"Jiahui Yu, Chengwei Zhang, Yingying Cheng, Yun-Fang Yang, Yuan-Bin She, Fengfan Liu, Weike Su, An Su","year":"2023","venue":"Digital Discovery","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Zhang et al., 2025] Shuai Zhang, Junfeng Fang, Xuqiang Li, hongxin xiang, ALAN XIA, Ye Wei, Wenjie Du, and Yang Wang. Iterative substructure extraction for molec- ular relational learning with interactive graph information bottleneck. In The Thirteenth International Conference on Learning Representations, 2025.","title":"Iterative substructure extraction for molecular relational learning with interactive graph information bottleneck","authors":"Shuai Zhang, Junfeng Fang, Xuqiang Li, Alan Xia, Ye Wei, Wenjie Du, Yang Wang","year":"2025","venue":"The Thirteenth International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Zheng et al., 2025] Yizhen Zheng, Huan Yee Koh, Jiaxin Ju, Anh TN Nguyen, Lauren T May, Geoffrey I Webb, and Shirui Pan. Large language models for scientific dis- covery in molecular property prediction. Nature Machine Intelligence, pages 1-11, 2025.","title":"Large language models for scientific discovery in molecular property prediction","authors":"Zheng","year":"2025","venue":"Nature Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Zhong et al., 2024] Yujie Zhong, Guangming Li, Jianxin Yang, et al. Learning motif-based graphs for drug-drug interaction prediction via local-global self-attention. Na- ture Machine Intelligence, 6:1094-1105, 2024.","title":"Learning motif-based graphs for drug-drug interaction prediction via local-global self-attention","authors":"Yujie Zhong, Guangming Li, Jianxin Yang","year":"2024","venue":"Nature Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Zhou et al., 2023] Gengmo Zhou, Zhifeng Gao, Qiankun Ding, Hang Zheng, Hongteng Xu, Zhewei Wei, Linfeng Zhang, and Guolin Ke. Uni-mol: a universal 3d molecular representation learning framework. 2023.","title":"Uni-mol: a universal 3d molecular representation learning framework","authors":"Gengmo Zhou, Zhifeng Gao, Qiankun Ding, Hang Zheng, Hongteng Xu, Zhewei Wei, Linfeng Zhang, Guolin Ke","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}