PepALD赋能大环肽设计
PepALD是一种自回归潜在扩散基础模型，用于从头生成大环肽。该模型使用结构化学嵌入表示HELM单体，在化学信息潜在空间中通过上下文条件扩散生成每个残基。它能在自回归生成过程中预测R基团感知的环闭合，并通过获胜者保护的扩散适应偏好优化与亲和力奖励对齐。实验表明PepALD在生成质量和奖励优化上优于代表性肽生成基线。
PepALD: Macrocyclic Peptide Generation via Autoregressive Latent Diffusion
Macrocyclic peptides are promising therapeutic candidates for intracellular targets, but their design requires simultaneous control over non-natural monomer chemistry, ring topology, membrane permeability, and target binding. Existing SMILES- or HELM-string generative models either operate in long atom-level sequence spaces or treat monomers as symbolic tokens with limited chemical grounding. We introduce PepALD, an Autoregressive Latent Diffusion (ALD) foundation model for \textit{de novo} macrocyclic peptide generation. The model represents HELM monomers with structured chemical embeddings, generates each residue through context-conditioned diffusion in chemically informed latent space, predicts R-group-aware ring closures during autoregressive generation, and aligns the denoiser to affinity rewards using winner-protected diffusion-adapted preference optimization. In silico experiments demonstrate PepALD's generation quality and reward-optimization performance against representative peptide generation baselines.