In the field of computational molecule generation, an essential task in the
discovery of new chemical compounds, fragment-based deep generative models are
a leading approach, consistently achieving state-of-the-art results in
molecular design benchmarks as of 2023. We present a detailed comparative
assessment of their architectures, highlighting their unique approaches to
molecular fragmentation and generative modeling. This review also includes
comparisons of output quality, generation speed, and the current limitations of
specific models. We also highlight promising avenues for future research that
could bridge fragment-based models to real-world applications.

在计算分子生成领域，基于片段的深度生成模型是发现新化学化合物的关键任务，具有领先的方法，在 2023 年一直在分子设计基准测试中获得最新成果。我们对这些模型的架构进行了详细的比较评估，重点介绍了它们在分子分割和生成建模方面的独特方法。本综述还包括了输出质量、生成速度以及特定模型的当前限制的比较，并突出了未来研究的有希望的方向，可以将基于片段的模型与实际应用相结合。