Automated BigSMILES conversion workflow and dataset for homopolymeric macromolecules
Abstract The simplified molecular-input line-entry system (SMILES) has been utilized in a variety of artificial intelligence analyses owing to its capability of representing chemical structures using line notation. However, its ease of representation is limited, which has led to the proposal of BigS...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-04-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-024-03212-4 |
| _version_ | 1797209560859017216 |
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| author | Sunho Choi Joonbum Lee Jangwon Seo Sung Won Han Sang Hyun Lee Ji-Hun Seo Junhee Seok |
| author_facet | Sunho Choi Joonbum Lee Jangwon Seo Sung Won Han Sang Hyun Lee Ji-Hun Seo Junhee Seok |
| author_sort | Sunho Choi |
| collection | DOAJ |
| description | Abstract The simplified molecular-input line-entry system (SMILES) has been utilized in a variety of artificial intelligence analyses owing to its capability of representing chemical structures using line notation. However, its ease of representation is limited, which has led to the proposal of BigSMILES as an alternative method suitable for the representation of macromolecules. Nevertheless, research on BigSMILES remains limited due to its preprocessing requirements. Thus, this study proposes a conversion workflow of BigSMILES, focusing on its automated generation from SMILES representations of homopolymers. BigSMILES representations for 4,927,181 records are provided, thereby enabling its immediate use for various research and development applications. Our study presents detailed descriptions on a validation process to ensure the accuracy, interchangeability, and robustness of the conversion. Additionally, a systematic overview of utilized codes and functions that emphasizes their relevance in the context of BigSMILES generation are produced. This advancement is anticipated to significantly aid researchers and facilitate further studies in BigSMILES representation, including potential applications in deep learning and further extension to complex structures such as copolymers. |
| first_indexed | 2024-04-24T09:56:39Z |
| format | Article |
| id | doaj.art-63fe68448051491a86d6dd1b2e2dc9b8 |
| institution | Directory Open Access Journal |
| issn | 2052-4463 |
| language | English |
| last_indexed | 2024-04-24T09:56:39Z |
| publishDate | 2024-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj.art-63fe68448051491a86d6dd1b2e2dc9b82024-04-14T11:07:22ZengNature PortfolioScientific Data2052-44632024-04-011111910.1038/s41597-024-03212-4Automated BigSMILES conversion workflow and dataset for homopolymeric macromoleculesSunho Choi0Joonbum Lee1Jangwon Seo2Sung Won Han3Sang Hyun Lee4Ji-Hun Seo5Junhee Seok6School of Electrical Engineering, Korea UniversityDepartment of Materials Science and Engineering, Korea UniversitySchool of Electrical Engineering, Korea UniversitySchool of Industrial Management Engineering, Korea UniversitySchool of Electrical Engineering, Korea UniversityDepartment of Materials Science and Engineering, Korea UniversitySchool of Electrical Engineering, Korea UniversityAbstract The simplified molecular-input line-entry system (SMILES) has been utilized in a variety of artificial intelligence analyses owing to its capability of representing chemical structures using line notation. However, its ease of representation is limited, which has led to the proposal of BigSMILES as an alternative method suitable for the representation of macromolecules. Nevertheless, research on BigSMILES remains limited due to its preprocessing requirements. Thus, this study proposes a conversion workflow of BigSMILES, focusing on its automated generation from SMILES representations of homopolymers. BigSMILES representations for 4,927,181 records are provided, thereby enabling its immediate use for various research and development applications. Our study presents detailed descriptions on a validation process to ensure the accuracy, interchangeability, and robustness of the conversion. Additionally, a systematic overview of utilized codes and functions that emphasizes their relevance in the context of BigSMILES generation are produced. This advancement is anticipated to significantly aid researchers and facilitate further studies in BigSMILES representation, including potential applications in deep learning and further extension to complex structures such as copolymers.https://doi.org/10.1038/s41597-024-03212-4 |
| spellingShingle | Sunho Choi Joonbum Lee Jangwon Seo Sung Won Han Sang Hyun Lee Ji-Hun Seo Junhee Seok Automated BigSMILES conversion workflow and dataset for homopolymeric macromolecules Scientific Data |
| title | Automated BigSMILES conversion workflow and dataset for homopolymeric macromolecules |
| title_full | Automated BigSMILES conversion workflow and dataset for homopolymeric macromolecules |
| title_fullStr | Automated BigSMILES conversion workflow and dataset for homopolymeric macromolecules |
| title_full_unstemmed | Automated BigSMILES conversion workflow and dataset for homopolymeric macromolecules |
| title_short | Automated BigSMILES conversion workflow and dataset for homopolymeric macromolecules |
| title_sort | automated bigsmiles conversion workflow and dataset for homopolymeric macromolecules |
| url | https://doi.org/10.1038/s41597-024-03212-4 |
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