通过混合引导策略提升分子生成的属性对齐与结构有效性。
MolGuidance: Advanced Guidance Strategies for Conditional Molecular Generation with Flow Matching
- 分离引导连续与离散分子模态，分别作用于速度场和逻辑值。
- 在QM9和QMe14S上实现新SOTA属性对齐性能，生成分子结构有效。
- 提供多种引导方法对比，适合药物发现与分子设计研究者。
条件分子生成的关键目标包括保证化学有效性、使生成分子匹配目标属性、促进结构多样性以及实现高效采样以支持发现。近期计算机视觉领域的生成模型引导策略可被迁移应用以达成这些目标。本文将分类器无关引导、自引导和模型引导等先进方法集成至基于SE(3)-等变流匹配的分子生成框架中。提出一种混合引导策略，分别对连续与离散分子模态进行引导——在速度场与预测逻辑值上操作，并通过贝叶斯优化联合优化其引导尺度。在QM9和QMe14S数据集上的实验表明，该方法在从头分子生成的属性对齐方面达到新状态最优水平，且生成分子具有高结构有效性。此外，系统比较了各类引导方法的优势与局限，为方法的广泛适用性提供了洞见。
原文摘要 · Abstract (English)
Key objectives in conditional molecular generation include ensuring chemical validity, aligning generated molecules with target properties, promoting structural diversity, and enabling efficient sampling for discovery. Recent advances in computer vision introduced a range of new guidance strategies for generative models, many of which can be adapted to support these goals. In this work, we integrate state-of-the-art guidance methods -- including classifier-free guidance, autoguidance, and model guidance -- in a leading molecule generation framework built on an SE(3)-equivariant flow matching process. We propose a hybrid guidance strategy that separately guides continuous and discrete molecular modalities -- operating on velocity fields and predicted logits, respectively -- while jointly optimizing their guidance scales via Bayesian optimization. Our implementation, benchmarked on the QM9 and QMe14S datasets, achieves new state-of-the-art performance in property alignment for de novo molecular generation. The generated molecules also exhibit high structural validity. Furthermore, we systematically compare the strengths and limitations of various guidance methods, offering insights into their broader applicability.
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