文献类型：期刊文献

英文题名：Macrocyclization of linear molecules by deep learning to facilitate macrocyclic drug candidates discovery

作者：Diao, Yanyan[1];Liu, Dandan[1];Ge, Huan[1];Zhang, Rongrong[1];Jiang, Kexin[1];Bao, Runhui[1];Zhu, Xiaoqian[1];Bi, Hongjie[1];Liao, Wenjie[1];Chen, Ziqi[1];Zhang, Kai[2];Wang, Rui[1];Zhu, Lili[1];Zhao, Zhenjiang[1];Hu, Qiaoyu[2];Li, Honglin[1,2,3]

机构：[1]East China Univ Sci & Technol, Sch Pharm, Shanghai Key Lab New Drug Design, Shanghai 200237, Peoples R China;[2]East China Normal Univ, Innovat Ctr AI & Drug Discovery, Shanghai 200062, Peoples R China;[3]Lingang Lab, Shanghai 200031, Peoples R China

年份：2023

卷号：14

期号：1

外文期刊名：NATURE COMMUNICATIONS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001040551400002)】；

基金：AcknowledgementsThis work was supported in part by the National Key Research and Development Program of China (2022YFC3400501); and the National Natural Science Foundation of China (81825020 and 82150208); H.L. was also sponsored by the National Program for Special Supports of Eminent Professionals and the National Program for Support of Top-notch Young Professionals.

语种：英文

摘要：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. Macrocyclization of bioactive acyclic molecules provides a potential avenue to yield novel chemical scaffolds with improved pharmacological properties. Here, the authors propose a deep learning based macrocyclization method to generate diverse macrocycles from a given acyclic molecule.