This study proposes MCEMOL (Multi-Constrained Evolutionary Molecular Design Framework), a molecular optimization approach integrating rule-based evolution with molecular crossover. MCEMOL employs dual-layer evolution: optimizing transformation rules at rule level while applying crossover and mutation to molecular structures. Unlike deep learning methods requiring large datasets and extensive training, our algorithm evolves efficiently from minimal starting molecules with low computational overhead. The framework incorporates message-passing neural networks and comprehensive chemical constraints, ensuring efficient and interpretable molecular design. Experimental results demonstrate that MCEMOL provides transparent design pathways through its evolutionary mechanism while generating valid, diverse, target-compliant molecules. The framework achieves 100% molecular validity with high structural diversity and excellent drug-likeness compliance, showing strong performance in symmetry constraints, pharmacophore optimization, and stereochemical integrity. Unlike black-box methods, MCEMOL delivers dual value: interpretable transformation rules researchers can understand and trust, alongside high-quality molecular libraries for practical applications. This establishes a paradigm where interpretable AI-driven drug design and effective molecular generation are achieved simultaneously, bridging the gap between computational innovation and practical drug discovery needs.

翻译：本研究提出MCEMOL（多约束进化分子设计框架），一种将规则进化与分子交叉相结合的分子优化方法。MCEMOL采用双层进化机制：在规则层面优化转化规则，同时在分子结构层面实施交叉与变异。与需要大量数据集和长时间训练的深度学习方法不同，本算法仅需少量初始分子即可实现高效进化，且计算开销较低。该框架整合了消息传递神经网络与综合性化学约束条件，确保分子设计过程兼具高效性与可解释性。实验结果表明，MCEMOL通过其进化机制提供透明的设计路径，同时生成有效、多样且符合目标特性的分子。该框架实现了100%的分子有效性，并具备高结构多样性与优异的类药性合规度，在对称性约束、药效团优化及立体化学完整性方面表现突出。与黑箱方法不同，MCEMOL提供双重价值：研究人员可理解且可信的可解释转化规则，以及适用于实际应用的高质量分子库。这建立了一种新范式，使可解释的人工智能驱动药物设计与高效分子生成得以同步实现，弥合了计算创新与实际药物研发需求之间的鸿沟。