Structure generators are widely used in de novo design studies and their performance substantially influences an outcome. Approaches based on the deep learning models and conventional atom-based approaches may result in invalid structures and fail to address their synthetic feasibility issues. On the other hand, conventional reaction-based approaches result in synthetically feasible compounds but novelty and diversity of generated compounds may be limited. Fragment-based approaches can provide both better novelty and diversity of generated compounds but the issue of synthetic complexity of generated structure was not explicitly addressed before. Here we developed a new framework of fragment-based structure generation that, by design, results in the chemically valid structures and provides flexible control over diversity, novelty, synthetic complexity and chemotypes of generated compounds. The framework was implemented as an open-source Python module and can be used to create custom workflows for the exploration of chemical space.

中文翻译：

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CReM：用于结构生成的化学合理突变框架


结构生成器广泛用于从头设计研究，其性能对结果有很大影响。基于深度学习模型的方法和传统的基于原子的方法可能会导致无效的结构，并且无法解决其合成可行性问题。另一方面，传统的基于反应的方法产生合成上可行的化合物，但生成的化合物的新颖性和多样性可能受到限制。基于片段的方法可以提供生成的化合物更好的新颖性和多样性，但是生成的结构的合成复杂性问题之前没有明确解决。在这里，我们开发了一种基于片段的结构生成的新框架，通过设计，可以产生化学上有效的结构，并提供对生成化合物的多样性、新颖性、合成复杂性和化学型的灵活控制。该框架作为开源 Python 模块实现，可用于创建用于探索化学空间的自定义工作流程。