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

Yanyan Diao; Feng Hu; Zihao Shen; Honglin Li

doi:10.1093/bioinformatics/btad012

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

Bioinformatics. 2023 Jan 1;39(1):btad012. doi: 10.1093/bioinformatics/btad012.

Authors

Yanyan Diao¹, Feng Hu¹, Zihao Shen¹, Honglin Li^{1

2

3}

Affiliations

¹ Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.
² Innovation Center for AI and Drug Discovery, East China Normal University, Shanghai 200062, China.
³ Lingang Laboratory, Shanghai 200031, China.

Abstract

Summary: 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'.

Availability and implementation: https://github.com/yydiao1025/MacFrag.

Supplementary information: Supplementary data are available at Bioinformatics online.

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

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Abstract

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