SynCoGen: Synthesizable 3D Molecule Generation via Joint Reaction and Coordinate Modeling

• URL: http://arxiv.org/abs/2507.11818v1
• Date: Wed, 16 Jul 2025 00:36:35 GMT
• Title: SynCoGen: Synthesizable 3D Molecule Generation via Joint Reaction and Coordinate Modeling
• Authors: Andrei Rekesh, Miruna Cretu, Dmytro Shevchuk, Vignesh Ram Somnath, Pietro Liò, Robert A. Batey, Mike Tyers, Michał Koziarski, Cheng-Hao Liu,
• Abstract summary: We present SynCoGen, a framework that combines masked graph diffusion and flow matching for synthesizable 3D molecule generation.<n>To train the model, we curated SynSpace, a dataset containing over 600K-aware building block graphs and 3.3M conformers.
• Score: 29.856853267388924
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ensuring synthesizability in generative small molecule design remains a major challenge. While recent developments in synthesizable molecule generation have demonstrated promising results, these efforts have been largely confined to 2D molecular graph representations, limiting the ability to perform geometry-based conditional generation. 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 containing over 600K synthesis-aware building block graphs and 3.3M conformers. SynCoGen achieves state-of-the-art performance in unconditional small molecule graph and conformer generation, and the model delivers competitive performance in zero-shot molecular linker design for protein ligand generation in drug discovery. Overall, this multimodal formulation represents a foundation for future applications enabled by non-autoregressive molecular generation, including analog expansion, lead optimization, and direct structure conditioning.

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