In 3D molecular discovery, optimizing conflicting physicochemical properties while strictly adhering to complex structural constraints constitutes a Constrained Multi-Objective Optimization Problem (CMOP). Solving this remains highly challenging: applying traditional Evolutionary Algorithm (EA) operators directly to 3D coordinates destroys chemical validity, whereas valid 3D diffusion models act as rigid generators unable to adapt to novel objectives without retraining. Moreover, employing traditional EA frameworks causes a severe loss of structural diversity, ultimately impairing algorithmic convergence. To overcome these challenges, we propose the Evolutionary-Guided Diffusion (EGD) operator, which executes crossover and mutation exclusively within the continuous noise space at an appropriate noise intensity. EGD enables topological hybridization while leveraging a pre-trained denoising network to project intermediate states back onto the valid chemical manifold. To tackle Multi-Objective Problems (MOPs), we introduce a Structure-Aware Environmental Selection (SAES) mechanism that explicitly enforces geometric diversity. Building upon this, to specifically solve CMOPs, we develop the Diffusion-based Evolutionary Molecular Optimization (DEMO) framework, utilizing a tri-population architecture with distinct responsibilities to safely navigate disjoint feasible regions. Extensive experiments across single-property targeting, unconstrained MOPs, multi-fragment constrained generation, and 3D protein-ligand docking demonstrate that DEMO comprehensively outperforms train-free guidance methods and EA baselines. Without any model retraining, DEMO successfully discovers highly diverse, chemically valid Pareto frontiers, establishing a robust paradigm for complex 3D molecular optimization.

翻译：在三维分子发现中，优化相互冲突的理化性质同时严格遵循复杂结构约束构成了一种约束多目标优化问题。解决这一问题极具挑战性：将传统进化算法算子直接应用于三维坐标会破坏化学有效性，而有效的三维扩散模型作为刚性生成器，若无重新训练则无法适应新目标。此外，采用传统进化算法框架会导致结构多样性严重丧失，最终损害算法收敛性。为克服这些挑战，我们提出进化引导扩散算子，该算子在适当噪声强度下仅在连续噪声空间内执行交叉与变异。EGD在实现拓扑杂交的同时，利用预训练去噪网络将中间状态投影回有效化学流形。为解决多目标问题，我们引入结构感知环境选择机制，明确施加几何多样性约束。在此基础上，为专门求解CMOP，我们开发了基于扩散的进化分子优化框架，采用具有明确职责分工的三群体架构以安全导航不连通的可行区域。跨单目标优化、无约束MOP、多片段约束生成及三维蛋白质-配体对接的大量实验表明，DEMO全面优于无训练引导方法及进化算法基线。无需任何模型重新训练，DEMO成功发现了高度多样化且化学有效的帕累托前沿，为复杂三维分子优化建立了鲁棒范式。