SynLaD同时搞定分子设计和合成路线，比以往只顾一头的模型更实用。
SynLaD是一个潜在扩散框架，用于小分子生成，统一了配体药物设计目标（做什么）与合成可行性（怎么做）。它通过学习一个潜在空间来解码3D结构和合成路径，结合了反应约束生成和药效团条件3D设计。在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上优于现有基线，能产生形状对齐的分子并附带可行合成方案。
SynLaD: Latent Diffusion for Generating Synthesizable Molecules Conditioned on 3D Pharmacophore Profiles
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.