文献类型：期刊文献

英文题名：MacFrag: segmenting large-scale molecules to obtain diverse fragments with high qualities

作者：Diao, Yanyan[1];Hu, Feng[1];Shen, Zihao[1];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：39

期号：1

外文期刊名：BIOINFORMATICS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000940926100074)】；

基金：This 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：Construction of high-quality fragment libraries by segmenting organic compounds is an important part of the drug discovery paradigm. This article presents a new method, MacFrag, for efficient molecule fragmentation. MacFrag utilized a modified version of BRICS rules to break chemical bonds and introduced an efficient subgraphs extraction algorithm for rapid enumeration of the fragment space. The evaluation results with ChEMBL dataset exhibited that MacFrag was overall faster than BRICS implemented in RDKit and modified molBLOCKS. Meanwhile, the fragments acquired through MacFrag were more compliant with the 'Rule of Three'.