Automated BigSMILES conversion workflow and dataset for homopolymeric macromolecules

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...

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Bibliographic Details
Published in:Scientific data 2024-04, Vol.11 (1), p.371-371, Article 371
Main Authors: Choi, Sunho, Lee, Joonbum, Seo, Jangwon, Han, Sung Won, Lee, Sang Hyun, Seo, Ji-Hun, Seok, Junhee
Format: Article
Language:English
Subjects:
639/638/455
639/638/455/958
639/638/455/959
Artificial intelligence
Automation
Data Descriptor
Deep learning
Humanities and Social Sciences
Macromolecules
multidisciplinary
R&D
Research & development
Science
Science (multidisciplinary)
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Summary: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.
ISSN:2052-4463
2052-4463
DOI:10.1038/s41597-024-03212-4