一次性生成可合成的3D分子结构，提升药物设计效率。
SynCoGen: Synthesizable 3D Molecule Generation via Joint Reaction and Coordinate Modeling
- 联合建模反应路径与原子坐标，实现端到端生成。
- 在120万分子块和750万构象数据上训练，性能领先。
- 无需评分函数，适合新药研发中的先导化合物设计。
可合成性仍是生成式分子设计中的关键瓶颈。尽管近期研究已在二维图结构中解决可合成性问题，但将其约束扩展至三维几何条件下的生成仍鲜有探索。本文提出SynCoGen（可合成共生成），一种统一框架，结合掩码图扩散与流匹配，实现可合成3D分子的联合生成。SynCoGen从分子构件、化学反应与原子坐标的联合分布中采样。为训练模型，我们构建了包含超过120万合成感知的分子块图和750万构象的SynSpace数据集家族。SynCoGen在无条件小分子图与构象共生成任务中达到当前最优性能；在药物发现中的蛋白质配体生成任务中，其近似模型在多种靶点下均表现出色，涵盖分子连接子设计与药效团条件生成，且不依赖任何评分函数。整体而言，这种多模态非自回归范式为类似分子设计应用——包括类似物拓展、先导优化与直接从头设计——提供了基础支持。
原文摘要 · Abstract (English)
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.
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