{"type":"ScholarlyArticle","title":"3D-FuM: Benchmarking 3D Molecule Learning with Functional Groups","authors":[{"name":"Tingwei Chen","slug":"tingwei-chen","given":"Tingwei","family":"Chen","affiliation":"Department of Computer Science , Virginia Tech , VA , USA"},{"name":"Jianpeng Chen","slug":"jianpeng-chen","given":"Jianpeng","family":"Chen","affiliation":"Department of Computer Science , Virginia Tech , VA , USA"},{"name":"Dawei Zhou","slug":"dawei-zhou","given":"Dawei","family":"Zhou","affiliation":"Department of Computer Science , Virginia Tech , VA , USA"}],"abstract":"Molecular graph representation learning plays a crucial role in various domains, such as drug discovery and chemical reaction prediction, where molecular graphs are typically depicted as 2D topological structures. However, recent insights highlight the critical role of 3D geometric information and functional groups in accurately predicting molecular properties, aspects often neglected in existing molecular graph benchmark datasets. To bridge the research gap, we introduce a comprehensive molecular learning benchmark named 3D-FUM, which incorporates both 3D geometric information and functional groups of a large number of molecules. 3D-FUM integrates 18 state-of-the-art algorithms and 19 evaluation metrics on three molecular learning tasks, including general molecule generation, conditional molecule generation, and property predictions. 3D-FUM, for the first time, take into consideration both 3D geometric information and molecular functional groups, which enables researchers and practitioners to effectively and impartially evaluate newly proposed methods in comparison to existing baselines across diverse datasets. Furthermore, we design a user interface for user-friendly interaction and development with the benchmark for evaluation metrics selection, parameter adjustment, and leaderboard comparison. To ensure accessibility and reproducibility, we opensource our benchmark 3D-FUM and experimental results at https://3dfunctiongroupmoleculedataset.github.io/3D-FuM/#/Home.","doi":"10.24963/ijcai.2024/997","url":"https://ijcai.org/proceedings/2024/997","pdf":"https://ijcai.org/proceedings/2024/0997.pdf","markdown":"https://ijcai.org/proceedings/2024/997.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/997","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Demo Track","track":"Demo Track","number":997,"pages":{"first":8635,"last":8639},"keywords":["Multidisciplinary Topics and Applications: MDA: Physical sciences","Multidisciplinary Topics and Applications: MDA: Bioinformatics"],"videos":[],"references":[{"position":1,"raw":"[Brandstetter et al., 2022] Johannes Brandstetter, Rob Hes- selink, Elise van der Pol, Erik J. Bekkers, and Max Welling. Geometric and physical quantities improve E(3) equivariant message passing. In The Tenth International Conference on Learning Representations, ICLR 2022, Vir- tual Event, April 25-29, 2022. OpenReview.net, 2022.","title":"Geometric and physical quantities improve E(3) equivariant message passing","authors":"Johannes Brandstetter, Rob Hesselink, Elise Van Der Pol, Erik J Bekkers, Max Welling","year":"2022","venue":"The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Chen et al., 2024] Jianpeng Chen, Yujing Wang, Ming Zeng, Zongyi Xiang, Bitan Hou, Yunhai Tong, Ole J. Mengshoel, and Yazhou Ren. Customizing graph neural networks using path reweighting. Information Sciences, 674:120681, 2024.","title":"Customizing graph neural networks using path reweighting","authors":"Jianpeng Chen, Yujing Wang, Ming Zeng, Zongyi Xiang, Bitan Hou, Yunhai Tong, Ole J Mengshoel, Yazhou Ren","year":"2024","venue":"Information Sciences","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Dai et al., 2021] Enyan Dai, Charu Aggarwal, and Suhang Wang. Nrgnn: Learning a label noise resistant graph neu- ral network on sparsely and noisily labeled graphs. In Pro- ceedings of the 27th ACM SIGKDD Conference on Knowl- edge Discovery & Data Mining, pages 227-236, 2021.","title":"Nrgnn: Learning a label noise resistant graph neural network on sparsely and noisily labeled graphs","authors":"Dai","year":"2021","venue":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Doshi and Chepuri, 2022] Siddhant Doshi and Sun- deep Prabhakar Chepuri. A computational approach to drug repurposing using graph neural networks. Computers in Biology and Medicine, 150:105992, 2022.","title":"A computational approach to drug repurposing using graph neural networks","authors":"Siddhant Doshi, Sundeep Prabhakar, Chepuri","year":"2022","venue":"Computers in Biology and Medicine","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Garcia Satorras et al., 2021] Victor Garcia Satorras, Emiel Hoogeboom, Fabian Fuchs, Ingmar Posner, and Max Welling. E (n) equivariant normalizing flows. Advances in Neural Information Processing Systems, 34:4181-4192, 2021.","title":"E (n) equivariant normalizing flows","authors":"Victor Garcia Satorras, Emiel Hoogeboom, Fabian Fuchs, Ingmar Posner, Max Welling","year":"2021","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Huang et al., 2023] Lei Huang, Hengtong Zhang, Tingyang Xu, and Ka-Chun Wong. Mdm: Molecular diffusion model for 3d molecule generation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 37, pages 5105-5112, 2023.","title":"Mdm: Molecular diffusion model for 3d molecule generation","authors":"Lei Huang, Hengtong Zhang, Tingyang Xu, Ka-Chun Wong","year":"2023","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Jinsong et al., 2024] Shao Jinsong, Jia Qifeng, Chen Xing, Yajie Hao, and Li Wang. Molecular fragmentation as a crucial step in the ai-based drug development pathway. Communications Chemistry, 7(1):20, 2024.","title":"Molecular fragmentation as a crucial step in the ai-based drug development pathway","authors":"Shao Jinsong, Jia Qifeng, Chen Xing, Yajie Hao, Li Wang","year":"2024","venue":"Communications Chemistry","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Kong et al., 2022] Xiangzhe Kong, Wenbing Huang, Zhix- ing Tan, and Yang Liu. Molecule generation by principal subgraph mining and assembling. Advances in Neural In- formation Processing Systems, 35:2550-2563, 2022.","title":"Molecule generation by principal subgraph mining and assembling","authors":"Kong","year":"2022","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Liao and Smidt, 2023] Yi-Lun Liao and Tess E. Smidt. Equiformer: Equivariant graph attention transformer for 3d atomistic graphs. In The Eleventh International Con- ference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net, 2023.","title":"Equiformer: Equivariant graph attention transformer for 3d atomistic graphs","authors":"Smidt ; ] Yi-Lun Liao, Tess E Liao, Smidt","year":"2023","venue":"The Eleventh International Conference on Learning Representations, ICLR 2023","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Ling et al., 2023] Yawen Ling, Jianpeng Chen, Yazhou Ren, Xiaorong Pu, Jie Xu, Xiaofeng Zhu, and Lifang He. Dual label-guided graph refinement for multi-view graph clustering. In Proceedings of the AAAI Conference on Ar- tificial Intelligence, volume 37, pages 8791-8798, 2023.","title":"Dual label-guided graph refinement for multi-view graph clustering","authors":"Yawen Ling, Jianpeng Chen, Yazhou Ren, Xiaorong Pu, Jie Xu, Xiaofeng Zhu, Lifang He","year":"2023","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Morselli Gysi et al., 2021] Deisy Morselli Gysi, Ítalo Do Valle, Marinka Zitnik, Asher Ameli, Xiao Gan, Onur Varol, Susan Dina Ghiassian, JJ Patten, Robert A Davey, Joseph Loscalzo, et al. Network medicine framework for identifying drug-repurposing opportunities for covid- 19. Proceedings of the National Academy of Sciences, 118(19):e2025581118, 2021.","title":"Network medicine framework for identifying drug-repurposing opportunities for covid-19","authors":"Deisy Morselli Gysi, Ítalo Do Valle, Marinka Zitnik, Asher Ameli, Xiao Gan, Onur Varol, Susan Dina Ghiassian, Robert A Patten, Joseph Davey, Loscalzo","year":"2021","venue":"Proceedings of the National Academy of Sciences","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Nakao et al., 2023] Atsuyuki Nakao, Yu Harabuchi, Satoshi Maeda, and Koji Tsuda. Exploring the quantum chemical energy landscape with gnn-guided artificial force. Jour- nal of Chemical Theory and Computation, 19(3):713-717, 2023.","title":"Exploring the quantum chemical energy landscape with gnn-guided artificial force","authors":"Nakao","year":"2023","venue":"Journal of Chemical Theory and Computation","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Patrini et al., 2017] Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, and Lizhen Qu. Making deep neural networks robust to label noise: A loss correction approach. In Proceedings of the IEEE confer- ence on computer vision and pattern recognition, pages 1944-1952, 2017.","title":"Making deep neural networks robust to label noise: A loss correction approach","authors":"Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, Lizhen Qu","year":"2017","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Patronov et al., 2022] Atanas Patronov, Kostas Papadopou- los, and Ola Engkvist. Has artificial intelligence impacted drug discovery? Artificial Intelligence in Drug Design, pages 153-176, 2022.","title":"Has artificial intelligence impacted drug discovery? Artificial Intelligence in Drug Design","authors":"Atanas Patronov, Kostas Papadopoulos, Ola Engkvist","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Peng et al., 2022] Xingang Peng, Shitong Luo, Jiaqi Guan, Qi Xie, Jian Peng, and Jianzhu Ma. Pocket2mol: Efficient molecular sampling based on 3d protein pockets. In Inter- national Conference on Machine Learning, pages 17644- 17655. PMLR, 2022.","title":"Pocket2mol: Efficient molecular sampling based on 3d protein pockets","authors":"Xingang Peng, Shitong Luo, Jiaqi Guan, Qi Xie, Jian Peng, Jianzhu Ma","year":"2022","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Polykovskiy et al., 2020] Daniil Polykovskiy, Alexander Zhebrak, Benjamin Sanchez-Lengeling, Sergey Golo- vanov, Oktai Tatanov, Stanislav Belyaev, Rauf Kurbanov, Aleksey Artamonov, Vladimir Aladinskiy, Mark Veselov, et al. Molecular sets (moses): a benchmarking platform for molecular generation models. Frontiers in pharmacology, 11:565644, 2020.","title":"Molecular sets (moses): a benchmarking platform for molecular generation models","authors":"Daniil Polykovskiy, Alexander Zhebrak, Benjamin Sanchez-Lengeling, Sergey Golovanov, Oktai Tatanov, Stanislav Belyaev, Rauf Kurbanov, Aleksey Artamonov, Vladimir Aladinskiy, Mark Veselov","year":"2020","venue":"Frontiers in pharmacology","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Ruddigkeit et al., 2012] Lars Ruddigkeit, Ruud Van Deursen, Lorenz C Blum, and Jean-Louis Reymond. Enumeration of 166 billion organic small molecules in the chemical universe database gdb-17. Journal of chemical information and modeling, 52(11):2864-2875, 2012.","title":"Enumeration of 166 billion organic small molecules in the chemical universe database gdb-17","authors":"Ruddigkeit","year":"2012","venue":"Journal of chemical information and modeling","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Schütt et al., 2017] Kristof Schütt, Pieter-Jan Kindermans, Huziel Enoc Sauceda Felix, Stefan Chmiela, Alexan- dre Tkatchenko, and Klaus-Robert Müller. Schnet: A continuous-filter convolutional neural network for model- ing quantum interactions. Advances in neural information processing systems, 30, 2017.","title":"Schnet: A continuous-filter convolutional neural network for modeling quantum interactions","authors":"Schütt","year":"2017","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Shi et al., 2021] Chence Shi, Shitong Luo, Minkai Xu, and Jian Tang. Learning gradient fields for molecular confor- mation generation. In International conference on ma- chine learning, pages 9558-9568. PMLR, 2021.","title":"Learning gradient fields for molecular conformation generation","authors":"Shi","year":"2021","venue":"International conference on machine learning","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Sterling and Irwin, 2015] Teague Sterling and John J Irwin. Zinc 15-ligand discovery for everyone. Journal of chemi- cal information and modeling, 55(11):2324-2337, 2015.","title":"Zinc 15-ligand discovery for everyone","authors":"Teague Sterling, John J Irwin","year":"2015","venue":"Journal of chemical information and modeling","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Thölke and Fabritiis, 2022] Philipp Thölke and Gianni De Fabritiis. Equivariant transformers for neural network based molecular potentials. In The Tenth International Conference on Learning Representations, ICLR 2022, Vir- tual Event, April 25-29, 2022. OpenReview.net, 2022.","title":"Equivariant transformers for neural network based molecular potentials","authors":"Philipp Thölke, Gianni De, Fabritiis","year":"2022","venue":"The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Unsleber et al., 2022] Jan P Unsleber, Stephanie A Grim- mel, and Markus Reiher. Chemoton 2.0: Autonomous ex- ploration of chemical reaction networks. Journal of Chem- ical Theory and Computation, 18(9):5393-5409, 2022.","title":"Chemoton 2.0: Autonomous exploration of chemical reaction networks","authors":"Jan P Unsleber, Stephanie A Grimmel, Markus Reiher","year":"2022","venue":"Journal of Chemical Theory and Computation","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Vignac et al., 2023] Clément Vignac, Igor Krawczuk, An- toine Siraudin, Bohan Wang, Volkan Cevher, and Pascal Frossard. Digress: Discrete denoising diffusion for graph generation. In Proceedings of the 11th International Con- ference on Learning Representations, 2023.","title":"Digress: Discrete denoising diffusion for graph generation","authors":"Clément Vignac, Igor Krawczuk, Antoine Siraudin, Bohan Wang, Volkan Cevher, Pascal Frossard","year":"2023","venue":"Proceedings of the 11th International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Wang et al., 2022] Yuyang Wang, Rishikesh Magar, Chen Liang, and Amir Barati Farimani. Improving molecular contrastive learning via faulty negative mitigation and de- composed fragment contrast. Journal of Chemical Infor- mation and Modeling, 62(11):2713-2725, 2022.","title":"Improving molecular contrastive learning via faulty negative mitigation and decomposed fragment contrast","authors":"Yuyang Wang, Rishikesh Magar, Chen Liang, Amir Barati, Farimani","year":"2022","venue":"Journal of Chemical Information and Modeling","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Xu et al., 2024] Dapeng Xu, Wei Tang, Feng Li, Ke Zhou, Junlin Wu, Alexander Fuqua, Yu-ting Chen, Dong Ju Lee, Jianting Qin, Andrea R Tao, et al. Insights on na3ps4 solid- state electrolyte dry films: Interfacial stability and dry room compatibility. ACS Energy Letters, 9:2293-2302, 2024.","title":"Insights on na3ps4 solidstate electrolyte dry films: Interfacial stability and dry room compatibility","authors":"Dapeng Xu, Wei Tang, Feng Li, Ke Zhou, Junlin Wu, Alexander Fuqua, Yu-Ting Chen, Dong Ju Lee, Jianting Qin, Andrea R Tao","year":"2024","venue":"ACS Energy Letters","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Zang et al., 2023] Xuan Zang, Xianbing Zhao, and Buzhou Tang. Hierarchical molecular graph self-supervised learn- ing for property prediction. Communications Chemistry, 6(1):34, 2023.","title":"Hierarchical molecular graph self-supervised learning for property prediction","authors":"Xuan Zang, Xianbing Zhao, Buzhou Tang","year":"2023","venue":"Communications Chemistry","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Zhang et al., 2021a] Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals. Understand- ing deep learning (still) requires rethinking generalization. Communications of the ACM, 64(3):107-115, 2021.","title":"Understanding deep learning (still) requires rethinking generalization","authors":"Zhang","year":"2021","venue":"Communications of the ACM","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Zhang et al., 2021b] Zaixi Zhang, Qi Liu, Hao Wang, Chengqiang Lu, and Chee-Kong Lee. Motif-based graph self-supervised learning for molecular property predic- tion. Advances in Neural Information Processing Systems, 34:15870-15882, 2021.","title":"Motif-based graph self-supervised learning for molecular property prediction","authors":"Zhang","year":"2021","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}