Constructive machine learning aims to create examples from its learned domain which are likely to exhibit similar properties. Here, a recurrent neural network was trained with the chemical structures of known cell‐migration modulators. This machine learning model was used to generate new molecules that mimic the training compounds. Two top‐scoring designs were synthesized, and tested for functional activity in a phenotypic spheroid cell migration assay. These computationally generated small molecules significantly increased the migration of medulloblastoma cells. The results further corroborate the applicability of constructive machine learning to the de novo design of druglike molecules with desired properties.

中文翻译：

---

由建设性机器学习设计的细胞迁移合成激活剂。

建设性机器学习旨在从其学习领域创建可能表现出类似属性的示例。在这里，使用已知细胞迁移调节剂的化学结构来训练循环神经网络。该机器学习模型用于生成模仿训练化合物的新分子。合成了两种得分最高的设计，并在表型球状细胞迁移测定中测试了功能活性。这些计算生成的小分子显着增加了髓母细胞瘤细胞的迁移。结果进一步证实了构建性机器学习对于具有所需特性的药物分子的从头设计的适用性。