用3D药效团条件生成可合成分子，一模型解决设计与合成难题
SynLaD: Latent Diffusion for Generating Synthesizable Molecules Conditioned on 3D Pharmacophore Profiles
- 在潜空间中联合学习分子三维结构与合成路径
- 生成的分子既符合药效团要求，又具备可行合成路线
- 适合药物研发人员快速筛选高潜力、可合成候选分子
我们提出 SynLaD，一种用于小分子生成的潜变量扩散框架，统一了基于配体的药物设计目标（要合成什么）与合成可及性（如何合成）。现有模型通常在两者间权衡，导致难以发现高分且可合成的分子。SynLaD 通过学习一个能解码为3D结构和合成路径的潜空间，结合反应约束生成与药效团条件化的3D设计。编码器将分子映射到潜表示，由两个解码头分别重建原子类型与坐标（几何头），以及以序列化反应符号输出合成路线（自回归合成头）。扩散变压器在学习的潜空间中生成新样本，条件于药效团轮廓。在多种生物活性配体的类似物生成任务中，SynLaD 在可合成性和多样性方面均优于现有基线，证明单一模型可生成结构对齐且合成可行的分子。
原文摘要 · Abstract (English)
We present SynLaD, a latent diffusion framework for small-molecule generation that unifies ligand-based drug design objectives (what to make) with synthetic accessibility (how to make it). Current models typically optimize one objective at the expense of the other, creating a bottleneck for discovering high-scoring and synthesizable molecules. SynLaD combines reaction-constrained generation with pharmacophore-conditioned 3D design by learning a latent space that decodes to both 3D structures and synthesis pathways. An encoder maps molecules to a latent representation used by two decoder heads: (i) a geometric head that reconstructs atom types and coordinates and (ii) an autoregressive synthesis head that outputs synthetic routes in a serialized, reaction-based notation. A diffusion transformer generates novel latents in the learned space, conditioned on pharmacophore profiles. Across analogue generation tasks for bioactive ligands, SynLaD outperforms existing baselines in synthesizable and diverse hit generation, demonstrating that a single model can produce shape-aligned molecules with feasible synthesis plans.
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