We challenge black-box purely deep neural approaches for molecules and graph generation, which are limited in controllability and lack formal guarantees. We introduce Neuro-Symbolic Graph Generative Modeling (NSGGM), a neurosymbolic framework that reapproaches molecule generation as a scaffold and interaction learning task with symbolic assembly. An autoregressive neural model proposes scaffolds and refines interaction signals, and a CPU-efficient SMT solver constructs full graphs while enforcing chemical validity, structural rules, and user-specific constraints, yielding molecules that are correct by construction and interpretable control that pure neural methods cannot provide. NSGGM delivers strong performance on both unconstrained generation and constrained generation tasks, demonstrating that neuro-symbolic modeling can match state-of-the-art generative performance while offering explicit controllability and guarantees. To evaluate more nuanced controllability, we also introduce a Logical-Constraint Molecular Benchmark, designed to test strict hard-rule satisfaction in workflows that require explicit, interpretable specifications together with verifiable compliance.

翻译：我们挑战用于分子和图生成的黑盒纯深度神经方法，这些方法在可控性方面存在局限且缺乏形式化保证。我们提出了神经符号图生成建模（NSGGM），这是一个神经符号框架，将分子生成重新定义为具有符号组装的支架与相互作用学习任务。自回归神经模型提出支架并精炼相互作用信号，而一个CPU高效的SMT求解器在强制执行化学有效性、结构规则和用户特定约束的同时构建完整图，从而产生构造正确的分子以及纯神经方法无法提供的可解释控制。NSGGM在无约束生成和约束生成任务上均表现出强大性能，证明了神经符号建模能够匹配最先进的生成性能，同时提供明确的可控性和保证。为了评估更细致的可控性，我们还引入了一个逻辑约束分子基准，该基准旨在测试需要明确、可解释规范以及可验证合规性的工作流程中严格硬规则的满足情况。