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.