Macrocyclization of linear molecules by deep learning to facilitate macrocyclic drug candidates discovery

Yanyan Diao, Dandan Liu, Huan Ge, Rongrong Zhang, Kexin Jiang, Runhui Bao, Xiaoqian Zhu, Hongjie Bi, Wenjie Liao, Ziqi Chen, Kai Zhang, Rui Wang, Lili Zhu, Zhenjiang Zhao, Qiaoyu Hu and Honglin Li ()
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Yanyan Diao: East China University of Science & Technology
Dandan Liu: East China University of Science & Technology
Huan Ge: East China University of Science & Technology
Rongrong Zhang: East China University of Science & Technology
Kexin Jiang: East China University of Science & Technology
Runhui Bao: East China University of Science & Technology
Xiaoqian Zhu: East China University of Science & Technology
Hongjie Bi: East China University of Science & Technology
Wenjie Liao: East China University of Science & Technology
Ziqi Chen: East China University of Science & Technology
Kai Zhang: East China Normal University
Rui Wang: East China University of Science & Technology
Lili Zhu: East China University of Science & Technology
Zhenjiang Zhao: East China University of Science & Technology
Qiaoyu Hu: East China Normal University
Honglin Li: East China University of Science & Technology

Nature Communications, 2023, vol. 14, issue 1, 1-15

Abstract: Abstract 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.

Date: 2023
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DOI: 10.1038/s41467-023-40219-8

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