Reaction-based de novo design refers to the in-silico generation of novel chemical structures by combining reagents using structural transformations derived from known reactions. The driver for using reaction-based transformations is to increase the likelihood of the designed molecules being synthetically accessible. We have previously described a reaction-based de novo design method based on reaction vectors which are transformation rules that are encoded automatically from reaction databases. A limitation of reaction vectors is that they account for structural changes that occur at the core of a reaction only, and they do not consider the presence of competing functionalities that can compromise the reaction outcome. Here, we present the development of a Reaction Class Recommender to enhance the reaction vector framework. The recommender is intended to be used as a filter on the reaction vectors that are applied during de novo design to reduce the combinatorial explosion of in-silico molecules produced while limiting the generated structures to those which are most likely to be synthesisable. The recommender has been validated using an external data set extracted from the recent medicinal chemistry literature and in two simulated de novo design experiments. Results suggest that the use of the recommender drastically reduces the number of solutions explored by the algorithm while preserving the chance of finding relevant solutions and increasing the global synthetic accessibility of the designed molecules.

基于反应的从头设计是指通过使用源自已知反应的结构转换组合试剂，在计算机上生成新的化学结构。使用基于反应的转换的驱动因素是增加设计分子可合成获得的可能性。我们之前已经描述了基于反应向量的基于反应的从头设计方法，反应向量是从反应数据库自动编码的转换规则。反应载体的局限性在于，它们只考虑发生在反应核心的结构变化，而没有考虑可能影响反应结果的竞争性官能团的存在。在这里，我们介绍了反应类推荐器的开发，以增强反应向量框架。推荐器旨在用作反应载体的过滤器，这些载体在从头设计期间应用，以减少产生的计算机内分子的组合爆炸，同时将生成的结构限制为最有可能合成的结构。已使用从最近的药物化学文献中提取的外部数据集和两个模拟的从头设计实验对推荐器进行了验证。结果表明，推荐器的使用大大减少了算法探索的解决方案的数量，同时保留了找到相关解决方案的机会并增加了设计分子的全局合成可访问性。