FragFM：基于片段级离散流匹配的高效分子生成分层框架
FragFM: Hierarchical Framework for Efficient Molecule Generation via Fragment-Level Discrete Flow Matching
AI总结：
FragFM通过片段级离散流匹配实现高效分子生成，优于原子级方法，并提出NPGen基准评估自然产物生成能力。
摘要：
我们引入了FragFM，一种通过片段级离散流匹配构建的新型分层框架，用于高效生成分子图。FragFM在片段层面生成分子，利用从粗到细的自动编码器来重建原子层面的细节。结合一种随机片段袋策略，以有效处理大规模的片段空间，我们的框架实现了更高效、可扩展的分子生成。我们证明，基于片段的方法在分子性质控制方面优于基于原子的方法，并通过对片段袋的条件化获得了额外的灵活性。我们还提出了一个自然产物生成基准（NPGen），用于评估现代分子图生成模型生成自然产物状分子的能力。由于自然产物已预先验证且不同于典型的药物样分子，我们的基准提供了更具挑战性但更有意义的评估，与药物发现相关。我们对FragFM与各种模型在多样化的分子生成基准上进行了比较研究，包括NPGen，展示了优越的性能。结果突显了基于片段的生成模型在大规模、属性感知分子设计中的潜力，为更高效地探索化学空间铺平了道路。
英文摘要：
We introduce FragFM, a novel hierarchical framework via fragment-level discrete flow matching for efficient molecular graph generation. FragFM generates molecules at the fragment level, leveraging a coarse-to-fine autoencoder to reconstruct details at the atom level. Together with a stochastic fragment bag strategy to effectively handle a large fragment space, our framework enables more efficient, scalable molecular generation. We demonstrate that our fragment-based approach achieves better property control than the atom-based method and additional flexibility through conditioning the fragment bag. We also propose a Natural Product Generation benchmark (NPGen) to evaluate the ability of modern molecular graph generative models to generate natural product-like molecules. Since natural products are biologically prevalidated and differ from typical drug-like molecules, our benchmark provides a more challenging yet meaningful evaluation relevant to drug discovery. We conduct a comparative study of FragFM against various models on diverse molecular generation benchmarks, including NPGen, demonstrating superior performance. The results highlight the potential of fragment-based generative modeling for large-scale, property-aware molecular design, paving the way for more efficient exploration of chemical space.
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