Interest in macrocycles as potential therapeutic agents has increased rapidly. Macrocyclization of bioactive acyclic molecules provides a potential avenue to yield novel chemical scaffolds, which can contribute to the improvement of the biological activity and physicochemical properties of these molecules. In this study, we propose a computational macrocyclization method based on Transformer architecture (which we name Macformer). Leveraging deep learning, Macformer explores the vast chemical space of macrocyclic analogues of a given acyclic molecule by adding diverse linkers compatible with the acyclic molecule. Macformer can efficiently learn the implicit relationships between acyclic and macrocyclic structures represented as SMILES strings and generate plenty of macrocycles with chemical diversity and structural novelty. In data augmentation scenarios using both internal ChEMBL and external ZINC test datasets, Macformer display excellent performance and generalisability. We showcase the utility of Macformer when combined with molecular docking simulations and wet lab based experimental validation, by applying it to the prospective design of macrocyclic JAK2 inhibitors.

人们对大环化合物作为潜在治疗剂的兴趣迅速增加。生物活性无环分子的大环化提供了产生新型化学支架的潜在途径，这有助于改善这些分子的生物活性和理化性质。在本研究中，我们提出了一种基于 Transformer 架构（我们将其命名为 Macformer）的计算大环化方法。利用深度学习，Macformer 通过添加与无环分子兼容的多种连接体，探索给定无环分子的大环类似物的广阔化学空间。Macformer 可以有效地学习以 SMILES 字符串表示的非环结构和大环结构之间的隐含关系，并生成大量具有化学多样性和结构新颖性的大环化合物。在使用内部 ChEMBL 和外部 ZINC 测试数据集的数据增强场景中，Macformer 显示出出色的性能和通用性。我们展示了 Macformer 与分子对接模拟和基于湿实验室的实验验证相结合的实用性，将其应用于大环 JAK2 抑制剂的前瞻性设计。