Publications

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

Preprints

• Efficient Evolutionary Search over Chemical Space with Large Language Models.
  Haorui Wang*, Marta Skreta*, …, Yuanqi Du†, Alán Aspuru-Guzik†, Kirill Neklyudov†, Chao Zhang†.
  In arXiv preprint arXiv:2406.16976.
• Large Language Models are Catalyzing Chemistry Education.
  Yuanqi Du*, Chenru Duan*, Andres Bran*, Anna Sotnikova, Yi Qu, Heather Kulik, Antoine Bosselut, Jinjia Xu, Philippe Schwaller.
  In ChemRxiv 10.26434/chemrxiv-2024-h722v.
• React-OT: Optimal Transport for Generating Transition State in Chemical Reactions.
  Chenru Duan*, Guan-Horng Liu*, Yuanqi Du*, Tianrong Chen, Qiyuan Zhao, Haojun Jia, Carla P. Gomes, Evangelos A. Theodorou, Heather J. Kulik.
  In arXiv preprint arXiv:2404.13430.
• Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints.
  Yuanqi Du*, Lingkai Kong*, Wenhao Mu*, Kirill Neklyudov, Valentin De Bortol, Haorui Wang, Dongxia Wu, Aaron Ferber, Yi-An Ma, Carla P. Gomes, Chao Zhang.
  In arXiv preprint arXiv:2402.18012.

Selected Publications

• Doob’s Lagrangian: A Sample-Efficient Variational Approach to Transition Path Sampling.
  Yuanqi Du*, Michael Plainer*, Rob Brekelmans*, Chenru Duan, Frank Noe, Carla P. Gomes, Alán Aspuru-Guzik, Kirill Neklyudov.
  In Neural Information Processing Systems (NeurIPS) 2024. (Spotlight)
• Navigating Chemical Space with Latent Flows. Check out our Demo!
  Guanghao Wei*, Yining Huang*, Chenru Duan, Yue Song†, Yuanqi Du†.
  In Neural Information Processing Systems (NeurIPS) 2024.
• Machine Learning-Aided Generative Molecular Design.
  Yuanqi Du*, Arian R. Jamasb*, Jeff Guo*, Tianfan Fu, Charles Harris, Yingheng Wang, Chenru Duan, Pietro Lio, Philippe Schwaller, Tom L. Blundell.
  In Nature Machine Intelligence 2024.
• Structure-based Drug Design with Equivariant Diffusion Models.
  Arne Schneuing*, Charles Harris*, Yuanqi Du*, Arian Jamasb, Ilia Igashov, Weitao Du, Carla P. Gomes, Tom Blundell, Pietro Lió, Max Welling, Michael Bronstein, Bruno Correia.
  In Nature Computational Science 2024. In Machine Learning for Structural Biology workshop NeurIPS 2022.
• 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. (Cover Story)
• 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 P. Gomes, Zhi-Ming Ma.
  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.
  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)
• 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.