AccFG: Accurate Functional Group Extraction and Molecular Structure Comparison
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| Title: | AccFG: Accurate Functional Group Extraction and Molecular Structure Comparison |
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| Authors: | Xuan Liu, Sarathkrishna Swaminathan, Dmitry Zubarev, Brandi Ransom, Nathaniel Park, Kristin Schmidt, Huimin Zhao |
| Publication Year: | 2025 |
| Collection: | Bath Spa University: Figshare |
| Subject Terms: | Biochemistry, Medicine, Molecular Biology, Sociology, Cancer, Computational Biology, Biological Sciences not elsewhere classified, Chemical Sciences not elsewhere classified, Information Systems not elsewhere classified, various data sets, processing functional groups, identify functional group, https :// github, carbon backbone ), functional group level, specific molecular patterns, molecular structure comparison, accfg generates refined, molecular structures, molecular pairs, molecular modality, level differences, useful tool, tool designed, textual descriptors, results demonstrate, present accfg, molecules based, molecule ’, manual examination |
| Description: | Functional groups are specific molecular patterns that influence a molecule’s chemical and physical properties. Identifying them is crucial for understanding the structure–activity relationships. Currently, accurately extracting functional groups from complex molecular structures remains a challenge. In this work, we present AccFG, a tool designed for precise functional group extraction and molecular structure comparison. AccFG generates refined and structured functional group descriptors for molecules based on a predefined library, while lowering the barrier to encoding and extending the library with new functional group definitions. Additionally, AccFG’s output includes identified functional groups and heterocycles with their corresponding mapped atom numbers, which enables molecular structure comparisons at the functional group level with alkane differences (differences in carbon backbone). We evaluated its performance on various data sets and examples with manual examination. The results demonstrate that AccFG can accurately extract functional groups from individual molecules and identify functional group-level differences between molecular pairs, effectively bridging the molecular modality and textual descriptors. We anticipate that AccFG will serve as a useful tool for processing functional groups, and its comprehensive, structured output enables fine-grained insights between molecular structures and properties. The code is available at https://github.com/xuanliugit/AccFG. |
| Document Type: | article in journal/newspaper |
| Language: | unknown |
| Relation: | https://figshare.com/articles/journal_contribution/AccFG_Accurate_Functional_Group_Extraction_and_Molecular_Structure_Comparison/29850026 |
| DOI: | 10.1021/acs.jcim.5c01317.s001 |
| Availability: | https://doi.org/10.1021/acs.jcim.5c01317.s001 https://figshare.com/articles/journal_contribution/AccFG_Accurate_Functional_Group_Extraction_and_Molecular_Structure_Comparison/29850026 |
| Rights: | CC BY-NC 4.0 |
| Accession Number: | edsbas.1842135 |
| Database: | BASE |
Nájsť tento článok vo Web of Science | Abstract: | Functional groups are specific molecular patterns that influence a molecule’s chemical and physical properties. Identifying them is crucial for understanding the structure–activity relationships. Currently, accurately extracting functional groups from complex molecular structures remains a challenge. In this work, we present AccFG, a tool designed for precise functional group extraction and molecular structure comparison. AccFG generates refined and structured functional group descriptors for molecules based on a predefined library, while lowering the barrier to encoding and extending the library with new functional group definitions. Additionally, AccFG’s output includes identified functional groups and heterocycles with their corresponding mapped atom numbers, which enables molecular structure comparisons at the functional group level with alkane differences (differences in carbon backbone). We evaluated its performance on various data sets and examples with manual examination. The results demonstrate that AccFG can accurately extract functional groups from individual molecules and identify functional group-level differences between molecular pairs, effectively bridging the molecular modality and textual descriptors. We anticipate that AccFG will serve as a useful tool for processing functional groups, and its comprehensive, structured output enables fine-grained insights between molecular structures and properties. The code is available at https://github.com/xuanliugit/AccFG. |
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| DOI: | 10.1021/acs.jcim.5c01317.s001 |