Publications

You can find my recent and full list of publications on my Google Scholar profile.

Preprints

• MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design.
  Yuanqi Du*, Tianfan Fu*, Jimeng Sun, Shengchao Liu.
  In arXiv preprint arXiv:2203.14500 (2022).

Selected Publications

• Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model.
  Chenru Duan, Yuanqi Du, Haojun Jia, Heather J. Kulik.
  In Nature Computational Science 2023.
• A new perspective on building efficient and expressive 3D equivariant graph neural networks.
  Weitao Du*, Yuanqi Du*, Limei Wang*, Dieqiao Feng, Guifeng Wang, Shuiwang Ji, Carla Gomes, Zhi-Ming Ma.
  In In Neural Information Processing Systems (NeurIPS) 2023. In TAG in ML workshop ICML 2023. (Oral)
• Path Integral Stochastic Optimal Control for Sampling Transition Paths.
  Lars Holdijk*, Yuanqi Du*, Priyank Jaini, Ferry Hooft, Bernd Ensing, Max Welling.
  In Neural Information Processing Systems (NeurIPS) 2023. In Machine Learning for Physical Sciences workshop NeurIPS 2022.
• M²Hub: Unlocking the Potential of Machine Learning for Materials Discovery.
  Yuanqi Du*, Yingheng Wang*, Yining Huang, Jianan Canal Li, Yanqiao Zhu, Tian Xie, Chenru Duan, John M. Gregoire, Carla P. Gomes.
  In Neural Information Processing Systems (NeurIPS) 2023 Datasets and Benchmarks track.
• A Flexible Diffusion Model.
  Weitao Du, Tao Yang, He Zhang, Yuanqi Du.
  In International Conference on Machine Learning (ICML) 2023.
• Scientific Discovery in the Age of Artificial Intelligence.
  Hanchen Wang*, Tianfan Fu*, Yuanqi Du*, Wenhao Gao+, Kexin Huang+, Ziming Liu+, …, Max Welling, Linfeng Zhang, Connor Coley, Yoshua Bengio, Marinka Zitnik.
  To appear in Nature (2023).
• ChemSpacE: Interpretable and Interactive Chemical Space Exploration.
  Yuanqi Du, Xian Liu, Shengchao Liu, Jieyu Zhang, Bolei Zhou.
  In Transactions on Machine Learning Research (TMLR) 2023. In ELLIS ML4Molecules workshop 2021. (Oral)
• Structure-based Drug Design with Equivariant Diffusion Models.
  Arne Schneuing*, Yuanqi Du*, Charles Harris, Arian Jamasb, Ilia Igashov, Weitao Du, Tom Blundell, Pietro Lió, Carla Gomes, Max Welling, Michael Bronstein, Bruno Correia.
  In arXiv preprint arXiv:2210.13695 (2022). In Machine Learning for Structural Biology workshop NeurIPS 2022.
• A Survey on Deep Graph Generation: Methods and Applications.
  Yuanqi Du*, Yanqiao Zhu*, Yinkai Wang*, Jieyu Zhang, Qiang Liu, Shu Wu.
  In First Learning on Graphs conference (LoG) 2022.
• Equivariant Graph Neural Networks with Complete Local Frames.
  Weitao Du*, He Zhang*, Yuanqi Du, Qi Meng, Wei Chen, Tie-Yan Liu, Nanning Zheng, Bin Shao.
  In Thirty-ninth International Conference on Machine Learning (ICML) 2022.
• Disentangled Spatiotemporal Graph Generative Models.
  Yuanqi Du*, Xiaojie Guo*, Hengning Cao, Yanfang Ye, Liang Zhao.
  In AAAI Conference on Artificial Intelligence (AAAI) 2022. (Oral)
• GraphGT: Machine Learning Datasets for Graph Generation and Transformation.
  Yuanqi Du*, Shiyu Wang*, Xiaojie Guo, Hengning Cao, Shujie Hu, Junji Jiang, Aishwarya Varala, Abhinav Angirekula, Liang Zhao.
  In Neural Information Processing Systems (NeurIPS) 2021 Datasets and Benchmarks track.
• Deep Generative Model for Spatial Networks.
  Xiaojie Guo*, Yuanqi Du*, Liang Zhao.
  In ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2021.