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. - 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.
Available soon. - 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. - Navigating Chemical Space with Latent Flows. Check out our Demo!
Guanghao Wei*, Yining Huang*, Chenru Duan, Yue Song†, Yuanqi Du†.
In arXiv preprint arXiv:2405.03987.
Selected Publications
- 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. - 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) - 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 P. 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.