基于片段的从头二维分子生成综述
A Review on Fragment-based De Novo 2D Molecule Generation
AI总结：
本文综述基于片段的深度生成模型在从头二维分子设计中的架构、碎片化与生成建模方法，并比较输出质量、速度、局限及实际应用前景。
摘要：
在计算分子生成领域，这是发现新化合物的一项核心任务；截至2023年，基于片段的深度生成模型是一种领先方法，并在分子设计基准中持续取得最先进结果。我们对其架构进行了详细的比较评估，重点介绍它们在分子碎片化和生成建模方面的独特方法。本综述还比较了输出质量、生成速度以及特定模型当前的局限性。我们也强调了未来研究中有前景的方向，这些方向可能将基于片段的模型连接到实际应用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
