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

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...

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Detalles Bibliográficos
Autores principales: Diao, Yanyan, Hu, Feng, Shen, Zihao, Li, Honglin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9872447/
https://www.ncbi.nlm.nih.gov/pubmed/36637187
http://dx.doi.org/10.1093/bioinformatics/btad012
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author Diao, Yanyan
Hu, Feng
Shen, Zihao
Li, Honglin
author_facet Diao, Yanyan
Hu, Feng
Shen, Zihao
Li, Honglin
author_sort Diao, Yanyan
collection PubMed
description 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.
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spelling pubmed-98724472023-01-31 MacFrag: segmenting large-scale molecules to obtain diverse fragments with high qualities Diao, Yanyan Hu, Feng Shen, Zihao Li, Honglin Bioinformatics Applications Note 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. Oxford University Press 2023-01-13 /pmc/articles/PMC9872447/ /pubmed/36637187 http://dx.doi.org/10.1093/bioinformatics/btad012 Text en © The Author(s) 2023. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Applications Note
Diao, Yanyan
Hu, Feng
Shen, Zihao
Li, Honglin
MacFrag: segmenting large-scale molecules to obtain diverse fragments with high qualities
title MacFrag: segmenting large-scale molecules to obtain diverse fragments with high qualities
title_full MacFrag: segmenting large-scale molecules to obtain diverse fragments with high qualities
title_fullStr MacFrag: segmenting large-scale molecules to obtain diverse fragments with high qualities
title_full_unstemmed MacFrag: segmenting large-scale molecules to obtain diverse fragments with high qualities
title_short MacFrag: segmenting large-scale molecules to obtain diverse fragments with high qualities
title_sort macfrag: segmenting large-scale molecules to obtain diverse fragments with high qualities
topic Applications Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9872447/
https://www.ncbi.nlm.nih.gov/pubmed/36637187
http://dx.doi.org/10.1093/bioinformatics/btad012
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