Starkly Speaking: SynCoGen: Synthesizable 3D Molecule Generation via Joint Reaction and Coordinate Modeling
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Hannes Stärk
Mar 9, 2026, 9:37:52 AM (10 days ago) Mar 9
to stark...@googlegroups.com
Hi together,
Todays reading group will be 1h earlier for non-us folks due to the timezone change that happened in the US on Sunday.
Speaker:
Andrei Rekesh and Miruna Cretu
Paper:
SynCoGen: Synthesizable 3D Molecule Generation via Joint Reaction and Coordinate Modeling https://arxiv.org/abs/2507.11818 (Andrei Rekesh, Miruna Cretu, Dmytro Shevchuk, Vignesh Ram Somnath, Pietro Liò, Robert A. Batey, Mike Tyers, Michał Koziarski, Cheng-Hao Liu)
Synthesizability remains a critical bottleneck in generative molecular design. While recent advances have addressed synthesizability in 2D graphs, extending these constraints to 3D for geometry-based conditional generation remains largely unexplored. In this work, we present SynCoGen (Synthesizable Co-Generation), a single framework that combines simultaneous masked graph diffusion and flow matching for synthesizable 3D molecule generation. SynCoGen samples from the joint distribution of molecular building blocks, chemical reactions, and atomic coordinates. To train the model, we curated SynSpace, a dataset family containing over 1.2M synthesis-aware building block graphs and 7.5M conformers. SynCoGen achieves state-of-the-art performance in unconditional small molecule graph and conformer co-generation. For protein ligand generation in drug discovery, the amortized model delivers superior performance in both molecular linker design and pharmacophore-conditioned generation across diverse targets without relying on any scoring functions. Overall, this multimodal non-autoregressive formulation represents a foundation for a range of molecular design applications, including analog expansion, lead optimization, and direct de novo design.
Meeting Details:
Every Monday at 9:00 PT / 12:00 ET / 18:00 CE(S)T
https://zoom.us/j/5775722530?pwd=ZzlGTXlDNThhUDZOdU4vN2JRMm5pQT09
Andrei Rekesh and Miruna Cretu
Paper:
SynCoGen: Synthesizable 3D Molecule Generation via Joint Reaction and Coordinate Modeling https://arxiv.org/abs/2507.11818 (Andrei Rekesh, Miruna Cretu, Dmytro Shevchuk, Vignesh Ram Somnath, Pietro Liò, Robert A. Batey, Mike Tyers, Michał Koziarski, Cheng-Hao Liu)
Synthesizability remains a critical bottleneck in generative molecular design. While recent advances have addressed synthesizability in 2D graphs, extending these constraints to 3D for geometry-based conditional generation remains largely unexplored. In this work, we present SynCoGen (Synthesizable Co-Generation), a single framework that combines simultaneous masked graph diffusion and flow matching for synthesizable 3D molecule generation. SynCoGen samples from the joint distribution of molecular building blocks, chemical reactions, and atomic coordinates. To train the model, we curated SynSpace, a dataset family containing over 1.2M synthesis-aware building block graphs and 7.5M conformers. SynCoGen achieves state-of-the-art performance in unconditional small molecule graph and conformer co-generation. For protein ligand generation in drug discovery, the amortized model delivers superior performance in both molecular linker design and pharmacophore-conditioned generation across diverse targets without relying on any scoring functions. Overall, this multimodal non-autoregressive formulation represents a foundation for a range of molecular design applications, including analog expansion, lead optimization, and direct de novo design.
Meeting Details:
Every Monday at 9:00 PT / 12:00 ET / 18:00 CE(S)T
https://zoom.us/j/5775722530?pwd=ZzlGTXlDNThhUDZOdU4vN2JRMm5pQT09
Meeting Details:
Every Monday at 9:00 PT / 12:00 ET / 18:00 CE(S)T
https://zoom.us/j/5775722530?pwd=ZzlGTXlDNThhUDZOdU4vN2JRMm5pQT09
Slack Workspace for discussion and paper voting:
https://join.slack.com/t/logag/shared_invite/zt-2zuxi7gd1-rLUgxg6gnCkhO7WlRsyElg
All information: Schedule of upcoming papers, recordings, mailing list:
https://portal.valencelabs.com/starklyspeaking
Hannes Stärk
Website: https://hannes-stark.com
PhD student at MIT
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