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.

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