Cyclic peptides represent a promising class of therapeutic compounds in modern drug discovery, often offering improved stability and binding affinity. However, the de novo design of cyclic peptides remains challenging because methods must identify pocket-adaptive cyclization patterns and linkage sites while simultaneously controlling drug-relevant properties. This challenge is particularly pronounced for recent generative models trained predominantly on linear peptide data, which may fail to capture cyclization-specific constraints. To address the limitation, we introduce APCyc, a target-aware de novo cyclic peptide generation framework that explicitly models cyclization and jointly optimizes multiple essential physicochemical properties. By using an expanded residue vocabulary and explicitly encoding cyclization-site and linkage-type information, APCyc learns cyclization-aware representations and leverages Bayesian posterior guidance to steer sampling toward cyclic peptides satisfying multiple property objectives. Experimental results demonstrate that our model learns target-dependent cyclization preferences, and enables effective and controllable multi-property optimization for cyclic peptide design. The source code of this paper is available at https://github.com/HKUSTGZ-ML4Health-Lab/APCyc.

翻译：环肽作为现代药物发现中一类极具前景的治疗分子，通常具有更优异的稳定性和结合亲和力。然而，环肽的从头设计仍然充满挑战，因为方法必须识别口袋适配的环化模式和连接位点，同时协同调控药物相关性质。这一挑战对于当前主要基于线性肽数据训练的生成模型尤为突出——这些模型往往难以捕捉环化特异性约束。为解决该局限性，我们提出APCyc——一种靶标感知的从头环肽生成框架，该框架显式建模环化过程并联合优化多种关键理化性质。通过采用扩展残基词汇表并显式编码环化位点与连接类型信息，APCyc学习环化感知表征，并借助贝叶斯后验引导将采样过程导向满足多目标性质的环肽。实验结果表明，我们的模型能够学习依赖靶标的环化偏好，并实现环肽设计中高效可控的多性质协同优化。本文源代码已开源至https://github.com/HKUSTGZ-ML4Health-Lab/APCyc。