提出可动态调整分子大小的生成模型，提升药物设计灵活性。
Generative Molecular Morphing for Flexible-Size Design via Unbalanced Optimal Transport
- 基于非平衡最优传输，动态调整分子原子数以适应目标属性
- 在保持性能的同时实现跨尺寸分子生成，超越固定原子数模型
- 适合需要灵活分子结构设计的药物发现场景
生成式分子设计的成功依赖于模型对高收益分子样本的可控性。由于许多分子性质与分子尺寸密切相关，准确捕捉性质与原子数的联合分布至关重要。然而，当前基于扩散和流模型的方法固定原子数，限制了其对这一复杂关系的建模能力。为此，我们提出Morph，一种基于几何图的柔性尺寸生成模型，支持条件与无条件3D分子设计。通过动态调整分子大小，Morph可无缝整合骨架等已有结构先验，显著提升属性控制能力。实验表明，Morph在性能上达到现有固定尺寸先进模型水平，同时具备前所未有的采样灵活性。我们在以往模型失效的分布外区域实现了成功生成，为分子设计的生成建模开辟新路径。
原文摘要 · Abstract (English)
The success of generative molecular design hinges on a model's steerability toward high-reward samples. Because many molecular properties are intrinsically linked to molecular size, accurately capturing the joint distribution of properties and the number of atoms is essential. However, current diffusion and flow-based models fix the number of atoms, which ultimately limits their ability to navigate this complex relationship. To address this, we introduce Morph, a flexible-size generative model for conditional and unconditional 3D molecular design based on geometric graphs. By dynamically adapting size, Morph can seamlessly integrate existing structural priors, like scaffolds, and significantly enhances property steering. We show that Morph matches current fixed-size state-of-the-art models while offering the benefit of unparalleled sampling flexibility. We demonstrate out-of-distribution generation in regimes where previous models fail, paving the way for enhanced generative modeling for molecular design.
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