Publications
You can find my recent and full list of 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.
Selected Publications
- Path Integral Stochastic Optimal Control for Sampling Transition Paths.
Lars Holdijk*, Yuanqi Du*, Priyank Jaini, Ferry Hooft, Bernd Ensing, Max Welling.
In arXiv preprint arXiv:2207.02149 (2022). In Machine Learning for Physical Sciencs workshop NeurIPS 2022. - 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). - 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. - Structural Causal Model for Molecular Dynamics Simulation.
Qi Liu, Yuanqi Du, Fan Feng, Qiwei Ye, Jie Fu.
In AI for Science workshop NeurIPS 2022. (Oral) - 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 ML4Molecules workshop 2022 (Oral). - 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
- A Flexible Diffusion Model.
Weitao Du, Tao Yang, He Zhang, Yuanqi Du.
In arXiv preprint arXiv:2206.10365 (2022). - 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 Systematic Survey of Molecular Pre-trained Models.
Jun Xia, Yanqiao Zhu, Yuanqi Du, Yue Liu, Stan Z.Li.
In arXiv preprint arXiv:2210.16484 (2022).