Me
Chenqing (Will) Hua
CS@McGill University & Mila
chenqing[dot]hua[at]mail.mcgill.ca
chenqing[dot]hua[at]mila.quebec

About

I am a master student at McGill & Mila working on AI, biology, data, supervised by Doina Precup and Guy Wolf. I received a bachelor degree in computer science (with first-class honours) at McGill & Mila, where I completed my thesis under supervision of William Hamilton on graph neural networks and heterophily. You may look into my CV.

I am working on the development of AI-driven enzyme design, contributing to the understanding of metabolic pathways and potentially aiding in therapeutic solutions. I always looks for collaborators to work togerther on enzyme discovery. I am also looking for full-time PhD positions starting in Fall 2025. For any collaboration, inquiry, question, discussion, or just a chat, you may feel free to reach out to me.

Research

  • AI for Protein and Enzyme Engineering
  • AI for Molecule Design
  • Graph Neural Network and Graph Transformer

Education

McGill University & Mila, Sep, 2022 - Dec, 2024
  • M.Sc in Computer Science (3.75/4.00)
McGill University & Mila, Sep, 2018 - May, 2022
  • B.Sc (First-Class Honours) in Computer Science (3.90/4.00)

News

  • One paper accepted to Chemical Science, Royal Society of Chemistry.
  • Two papers accepted to 38th Conference on Neural Information Processing Systems AIDrugX.
  • A tutorial on enzyme discovery with ReactZyme and EnzymeFlow is released. [Document]
  • One paper accepted to 38th Conference on Neural Information Processing Systems.
  • Give a talk on protein-protein interaction prediction to Jian Tang's group. [Slide]
  • Give a talk on structure-based molecule design at LoG2023 Montreal. [Slide][Video]
  • One paper accepted to 2nd Learning on Graphs Conference.
  • One paper accepted to 37th Conference on Neural Information Processing Systems.
  • One paper accepted to 12th International Conference on Complex Networks and their Applications.
  • One paper accepted to 36th Conference on Neural Information Processing Systems GLFrontiers (one Oral).
  • Two papers accepted to 36th Conference on Neural Information Processing Systems (one Spotlight).
    •

Publications (by topic and date)

Protein and Enzyme Engineering:
EnzymeCAGE: A Geometric Deep Learning Model for Catalytic-Specified Enzyme Retrieval and Function Prediction with Evolutionary Insights
Liu, Y., Hua, C., Zeng, T., Rao, J., Wu, R., Coley, C., Zheng, S.
Submitted to Nature Methods, 2024
[ Paper]
Reaction-conditioned De Novo Enzyme Design with GENzyme
Hua, C.*, Lu, J.*, Liu, Y., Zhang, O., Tang, J., Ying, R., Jin, W., Wolf, G., \\ Precup, D., Zheng, S.
Preprint, 2024
[ Paper]
EnzymeFlow: Generating Reaction-specific Enzyme Catalytic Pockets through Flow Matching and Co-Evolutionary Dynamics
Hua, C., Liu, Y., Zhang, D., Zhang, O., Luan, S., Yang, K.K., Wolf, G., Precup, D., Zheng, S.
Submitted to ICLR2025; Neural Information Processing Systems AIDrugX, 2024
[ Paper]
Reactzyme: A Benchmark for Enzyme-Reaction Prediction
Hua, C.*, Zhong, B.*, Luan, S., Hong, L., Wolf, Guy., Precup, D., Zheng, S.
Neural Information Processing Systems, 2024
[ Paper]
Effective Protein-Protein Interaction Exploration with PPIretrieval
Hua, C., Coley, C., Wolf, G., Precup, D., Zheng, S.
Neural Information Processing Systems AIDrugX, 2024
[ Paper]
Molecule Design:
FragGen: Towards 3D Geometry Reliable Fragment-based Molecular Generation
Zhang, O., Huang, Y., Cheng, S., Yu, M., Zhang, X., Lin, H., Zeng, Y., Wang, M., Wu, Z., Zhao, H., Hua, C.u, Kang Y., Cui, S., Pan, P., Hsieh, CY., Hou T.
Chemical Science, Royal Society of Chemistry, 2024
[ Paper]
ECloudGen: Access to Broader Chemical Space for Structure-based Molecule Generation
Zhang, O., Jin J., Lin H., Zhang J., Hua, C., Huang Y., Zhao H., Hsieh, CY., Hou T.
Submitted to Nature Machine Intelligence, 2024
[ Paper]
MUDiff: Unified Diffusion for Complete Molecule Generation
Hua, C., Luan, S., Xu, M., Ying, R., Fu, J., Ermon, S., Precup, D.
Learning On Graphs Conference, 2023
[ Paper]
Graph Neural Network Design:
Revisiting Heterophily For Graph Neural Networks
Luan, S., Hua, C., Lu, Q., Zhu, Jia., Zhao, M., Zhang, S., Chang, X. W., Precup, D.
Neural Information Processing Systems, 2022 (Spotlight)
[ Paper]
High-Order Pooling for Graph Neural Networks with Tensor Decomposition
Hua, C., Rabusseau, G., Tang, J.
Neural Information Processing Systems, 2022
[ Paper]
Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Networks
Luan, S.*, Zhao, M.*, Hua, C.*, Chang, X. W., Precup, D
Neural Information Processing Systems GLFrontiers, 2022 (Oral)
[ Paper]
Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification?
Luan, S.*, Hua, C.*, Chang, X. W., Precup, D
Preprint, 2021
[ Paper]
Graph Neural Network Principle:
Are Heterophilic GNNs and Homophily Metrics Really Effective? Evaluation Pitfalls and New Benchmarks
Luan, S., Lu, Q., Hua, C., Wang, X., Zhu, J., Chang, XW., Wolf, G., Tang, J.
Submitted to LoG, 2024
[ Paper]
The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges
Luan, S., Hua, C., Lu, Q., Ma, L., Wu, L., Wang, X., Xu, M., Chang, XW., Precup, D., Ying R., Li, SZ., Tang, J., Wolf, G., Jegelka, S.
Preprint, 2024
[ Paper]
When Do Graph Neural Networks Help with Node Classification? Investigating the Homophily Principle on Node Distinguishability
Luan, S., Hua, C., Xu, M., Lu, Q., Zhu, J., Chang, XW., Fu, J., Leskovec, J., Precup, D.
Neural Information Processing Systems, 2023
[ Paper]
When Do We Need GNN for Node Classification?
Luan, S., Hua, C., Lu, Q., Zhu, Jia., Chang, X. W., Precup, D.
International Conference on Complex Networks and their Applications, 2023
[ Paper]
Graph neural networks intersect probabilistic graphical models: A survey
Hua, C., Luan, S., Zhang, Q., Fu, J., Wolf, G.
Submitted to ICASSP, 2024
[ Paper]

Honor & Award

  • Scholarship of FACS-Acuity Project, Ministere de l'Economie et de l'Innovation Canada, May, 2022 - Present
  • ICML2023 Travel Award, July 2023
  • Neurips 2022 Scholar Award, Dec 2022
  • Scholarship of CIFAR AI chair program, Canadian Institute for Advanced Research, May, 2021 - Aug, 2021
  • Scholarship of Discovery program, Natural Sciences and Engineering Research Council of Canada, May, 2021 - Aug, 2021
  • Funding of Calcul Quebec, Calcul Quebec, May, 2021 - Aug, 2021
  • Funding of Digital Research Alliance of Canada, Digital Research Alliance of Canada, May, 2021 - Aug, 2021
  • Funding of NVIDIA, NVIDIA, May, 2021 - Aug, 2021
    •

Internship

Sep, 2023 - Now
Research Intern @ Aureka Biotechnologies
Protein and Enzyme Engineering, Generative Model
Supervisor: Shuangjia Zheng
May, 2022 - Dec, 2022
Research Intern @ Mila
Generative Flow Network, Molecule Design
Supervisor: Yoshua Bengio
Jun, 2021 - Jan, 2022
Research Intern @ Mila
Graph Neural Network, Tensor Method
Supervisor: Jian Tang and Guillaume Rabusseau
Dec, 2020 - Apr, 2021
Research Intern @ Mila
Graph Neural Network, Heterophily
Supervisor: William Hamiltom

Teaching

Teaching Assistant
  • MGSC695 Intro to AI & Deep Learning II, McGill University, Summer 2022
  • MGSC673 Intro to AI & Deep Learning I, McGill University, Winter 2022
  • MATH340 Discrete Mathematics, McGill University, Winter 2020
    •

Service

Reviewer: ICML2022, LoG2022, NeurIPS2022 AI4Mat, NeurIPS2022 GLFrontier, ICML2023, NeurIPS2023, KDD2023 PhD Consortium, LoG2023, ICLR2024, ICLR2024 GEM, ICLR2024 AGI, ICML2024, LoG2024, NeurIPS2024, AAAI2025, ICLR2025
Area Chair: NeurIPS2023 GLFrontier
Organizer: LoG2023 Montreal

Miscellaneous

🏃‍♀️ I lift weights and do cardios.
🥇 In personal ethics, I deeply respect the intrinsic value of every individual, regardless of background, race, or beliefs. My ethical principles align closely with the institute's goals, guiding my convictions. These ethics are not just words; they form the foundation of my beliefs for research. It is crucial that our AI endeavors are grounded in a strong commitment to individual rights.
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