Biogenic compounds are important materials for drug discovery and chemical biology. In this work, we report a quasi-biogenic molecule generator (QBMG) to compose virtual quasi-biogenic compound libraries by means of gated recurrent unit recurrent neural networks. The library includes stereo-chemical properties, which are crucial features of natural products. QMBG can reproduce the property distribution of the underlying training set, while being able to generate realistic, novel molecules outside of the training set. Furthermore, these compounds are associated with known bioactivities. A focused compound library based on a given chemotype/scaffold can also be generated by this approach combining transfer learning technology. This approach can be used to generate virtual compound libraries for pharmaceutical lead identification and optimization.

中文翻译：

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QBMG：具有深度递归神经网络的准生物分子生成器。

生物化合物是药物发现和化学生物学的重要材料。在这项工作中，我们报告了一个准生物分子生成器（QBMG），它通过门控递归单元递归神经网络组成虚拟的拟生物化合物库。该库包含立体化学性质，这是天然产物的关键特征。QMBG可以重现基础训练集的属性分布，同时能够在训练集之外生成逼真的新颖分子。此外，这些化合物与已知的生物活性有关。通过结合转移学习技术的这种方法，也可以生成基于给定化学型/支架的聚焦化合物库。此方法可用于生成虚拟化合物库，以进行药物先导识别和优化。