Tony Shen
I am a PhD student in Computing Science at Simon Fraser University, supervised by Dr. Martin Ester. My research focuses on structure-based drug design with generative flow networks and flow matching models.
Currently, I am also a visiting researcher at Northeastern University and Broad Institute of MIT and Harvard, where I work on foundation models for bio-molecular generation and antibiotics discovery applications under the supervision of Dr. Wengong Jin.
Previously, I worked as a Machine Learning Consultant at Transcripta Bio, where I conducted experiments on generative models and large scale virtual screening. Before that, I interned at Recursion, developing the map of Biology and Google, working on AI models for search.
Research Interests
My research is centered around AI for Drug Discovery, with a specific focus on:
- Foundation models for bio-molecular generation
- Generative Flow Networks (GFlowNets)
- Flow Matching and Diffusion Models
- Structure-based drug design
news
Mar 05, 2025 | Our paper “Compositional Flows for 3D Molecule and Synthesis Pathway Co-design” has been accepted at GEM and AI4MAT workshops at ICLR 2025. |
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Jan 22, 2025 | Our paper “Generative Flows on Synthetic Pathway for Drug Design” has been accepted as a poster at ICLR 2025. |
Oct 13, 2024 | Our paper “Generative Flows on Synthetic Pathway for Drug Design” has been accepted as a poster at the AIDrugX workshop at NeurIPS 2024. |
Sep 03, 2024 | Our paper “TacoGFN: Target-conditioned GFlowNet for Structure-based Drug Design” has been accepted for publication in TMLR. |
Oct 27, 2023 | Our paper “TacoGFN: Target Conditioned GFlowNet for Drug Design” has been accepted as a spotlight presentation at the GenBio workshop and as a poster at the AI4D3 workshop at NeurIPS 2023. |
selected publications
- ICLR WorkshopCompositional Flows for 3D Molecule and Synthesis Pathway Co-designUnder conference submission, ICLR GEM and AI4MAT workshop, 2025
- ICLRGenerative Flows on Synthetic Pathway for Drug DesignInternational Conference on Learning Representations (ICLR), 2025
- TMLRTacoGFN: Target Conditioned GFlowNet for Structure-Based Drug DesignTransactions on Machine Learning Research (TMLR), 2024