AI 中文总结
研究针对分子图扩散中统一损坏计划的不足，提出MotifRole-Diff方法，根据去噪难度和扰动影响分配掩码率，经实验验证该方法能提升生成有效性并降低FCD，表明结构感知损坏是更优掩码策略。
AI 中文摘要
分子图生成的掩码离散扩散通常对无损图到序列表示中的所有令牌应用统一的损坏计划，将结构异质的分子成分视为重构难度和重要性相同。然而，不同分子图令牌角色在去噪难度及其对解码分子的影响上存在很大差异，因此需要特定角色的损坏策略。我们引入了MotifRole-Diff，这是一种角色感知损坏过程，它根据经验测量的去噪难度和图级扰动影响来分配掩码率，同时保留模型架构、干净序列空间和无损分子图解码器。我们将计划选择公式化为在令牌角色之间对固定掩码预算进行风险最优分配。我们的定理表征了建模的角色加权残余风险的最优性，同时通过实验评估下游生成性能。在匹配的架构、训练预算和采样计算下，MotifRole-Diff将QM9上的有效性从0.905提高到0.944，同时将FCD从1.701降低到1.609；在MOSES上，有效性从 0.920提高到0.938，同时将FCD从2.125降低到1.850。按角色诊断进一步表明跨分子图令牌类别的重构得到了改善。总之，这些匹配计算结果表明，对于序列化分子图扩散，结构感知损坏是比统一计划更有效的掩码策略。
英文摘要
Masked discrete diffusion for molecular graph generation typically applies a uniform corruption schedule to all tokens in a lossless graph-to-sequence representation, implicitly treating structurally heterogeneous molecular components as equally difficult and equally important to reconstruct. However, different molecular graph token roles exhibit substantial variation in denoising difficulty and their influence on the decoded molecule, motivating role-specific corruption strategies. We introduce MotifRole-Diff, a role-aware corruption process that allocates masking rates according to empirically measured denoising difficulty and graph-level perturbation impact while preserving the model architecture, clean sequence space, and lossless molecular-graph decoder. We formulate schedule selection as the risk-optimal allocation of a fixed masking budget across token roles. Our theorem characterizes optimality for the modeled role-weighted residual risk, while downstream generation performance is evaluated empirically. Under matched architecture, training budget, and sampling compute, MotifRole-Diff improves validity on QM9 from 0.905 to 0.944 while reducing FCD from 1.701 to 1.609, and on MOSES improves validity from 0.920 to 0.938 while reducing FCD from 2.125 to 1.850. Role-wise diagnostics further show improved reconstruction across molecular graph token categories. Together, these matched-compute results indicate that structurally informed corruption is a more effective masking strategy than uniform schedules for serialized molecular graph diffusion.
