Publications

You can also find my recent publications on my Google Scholar profile. My publications center around several topics: from machine learning perspective, it includes deep generative models, disentanglement learning, representation learning, and geometric deep learning; from scientific perspective, it includes molecule design, molecular modeling, and molecular simulation.

Publications

• Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Biomolecular Structures and Interaction Networks.
  Arian Rokkum Jamasb, Ramon Viñas Torné, Eric J Ma, Yuanqi Du, Charles Harris, Kexin Huang, Dominic Hall, Pietro Lio, Tom Leon Blundell. In Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS) 2022.
• Audio-Driven Co-Speech Gesture Image Generation.
  Xian Liu, Qianyi Wu, Hang Zhou, Yuanqi Du, Wayne Wu, Dahua Lin, Ziwei Liu.
  In Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS) 2022.
• Multi-objective Deep Data Generation with Correlated Property Control.
  Shiyu Wang, Xiaojie Guo, Xuanyang Lin, Bo Pan, Yuanqi Du, et al.
  In Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS) 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.
• GAUCHE: A Library for Gaussian Processes and Bayesian Optimisation in Chemistry.
  Ryan-Rhys Griffiths*, Leo Klarner*, Henry Moss*, Aditya Ravuri*, Sang T. Truong*, Bojana Ranković*, Yuanqi Du*, et al. In ReALML workshop ICML 2022, AI for Science workshop ICML 2022.
• ChemSpacE: Toward Steerable and Interpretable Chemical Space Exploration.
  Yuanqi Du, Xian Liu, Shengchao Liu, Jieyu Zhang, Bolei Zhou.
  In MLDD workshop ICLR 2022, ELLIS ML4Molecules workshop 2021. (Oral)
• Disentangled Spatiotemporal Graph Generative Models.
  Yuanqi Du*, Xiaojie Guo*, Hengning Cao, Yanfang Ye, Liang Zhao.
  In Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI) 2022. (Oral)
  Presented in NeurIPS 2021 DGMs workshop.
• Small Molecule Generation via Disentangled Representation Learning.
  Yuanqi Du, Xiaojie Guo, Yinkai Wang, Amarda Shehu, Liang Zhao.
  In Bioinformatics 2022.
• 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.
• Physics-Augmented Learning: A New Paradigm Beyond Physics-Informed Learning.
  Ziming Liu, Yunyue Chen, Yuanqi Du, Max Tegmark.
  In NeurIPS AI for Science workshop 2021.
• Property Controllable Variational Autoencoder via Invertible Mutual Dependence.
  Xiaojie Guo, Yuanqi Du, Liang Zhao.
  In International Conference on Learning Representations (ICLR) 2021.

Preprints

• Path Integral Stochastic Optimal Control for Sampling Transition Paths.
  Lars Holdijk*, Yuanqi Du*, Priyank Jaini, Ferry Hooft, Bernd Ensing, Max Welling.
• Featurizations Matter: A Multiview Contrastive Learning Approach to Molecular Pretraining.
  Yanqiao Zhu*, Dingshuo Chen*, Yuanqi Du*, Yingze Wang, Qiang Liu, Shu Wu.
• 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.
• A Survey on Deep Graph Generation: Methods and Applications.
  Yuanqi Du*, Yanqiao Zhu*, Yinkai Wang*, Jieyu Zhang, Qiang Liu, Shu Wu.
  In arXiv preprint arXiv:2203.06714 (2022).