Optimizing conflicting molecular properties while strictly adhering to complex 3D structural constraints constitutes a challenging Constrained Multi-Objective Optimization Problem (CMOP). Traditional Evolutionary Algorithms (EAs) destroy chemical valency in 3D space, whereas 3D diffusion models act as rigid generators requiring costly retraining for novel objectives. To bridge this gap, we propose a progressive algorithmic suite. First, we introduce the Evolutionary-Guided Diffusion (EGD) operator, which executes crossover and mutation at an optimally calibrated noise level, leveraging a pre-trained denoising network to project chimeric states back onto the valid chemical manifold. Second, to combat the severe loss of molecular structural diversity inherent in traditional EMO frameworks, we design a Structure-Aware Environmental Selection (SAES) mechanism that explicitly enforces structural distinctiveness. Finally, synergizing EGD and SAES, we develop the Diffusion-based Evolutionary Molecular Optimization (DEMO) framework for CMOPs. To safely navigate disjoint feasible regions, DEMO employs a tri-population architecture with distinct goals: exploring novel chemical scaffolds, refining partially assembled intermediates, and fine-tuning perfectly feasible elite molecules. Extensive experiments across single-property targeting, unconstrained MOPs, multi-fragment CMOPs, and 3D protein-ligand docking demonstrate that our method comprehensively outperforms state-of-the-art baselines and traditional EMO frameworks. Operating entirely zero-shot, this suite consistently discovers highly diverse, chemically valid Pareto frontiers.

翻译：在严格遵循复杂三维结构约束的同时优化相互冲突的分子性质，构成了具有挑战性的约束多目标优化问题。传统进化算法会破坏三维空间中的化学价键，而三维扩散模型作为刚性生成器，需为新的目标函数进行昂贵的重训练。为弥合这一鸿沟，我们提出了一套渐进式算法体系。首先，引入进化引导扩散算子，该算子在最优化校准的噪声水平上执行交叉与变异操作，利用预训练的去噪网络将嵌合态投影回有效化学流形。其次，为应对传统进化多目标框架中固有的分子结构多样性严重丧失问题，我们设计了结构感知环境选择机制，显式强制保持结构独特性。最后，通过协同进化引导扩散算子与结构感知环境选择机制，我们开发了面向约束多目标优化问题的基于扩散的进化分子优化框架。为安全导航互不连通的可行区域，该框架采用具有不同目标的三群体架构：探索新型化学骨架、优化部分组装的中间体、微调完全可行的精英分子。针对单性质目标、无约束多目标、多片段约束多目标以及三维蛋白质-配体对接任务的广泛实验表明，本方法全面超越现有最优基准方法与传统进化多目标框架。该体系完全以零样本方式运行，持续发现高多样性、化学有效的帕累托前沿。