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Automated BigSMILES conversion workflow and dataset for homopolymeric macromolecules.

Sunho Choi¹, Joonbum Lee², Jangwon Seo¹

¹School of Electrical Engineering, Korea University, Seoul, South Korea.

Scientific Data

|April 11, 2024

Summary

This study introduces an automated workflow to convert Simplified Molecular-Input Line-Entry System (SMILES) into BigSMILES, enabling easier representation of macromolecules. This facilitates broader research and development applications in cheminformatics and artificial intelligence.

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Synthesis of Information-bearing Peptoids and their Sequence-directed Dynamic Covalent Self-assembly

Synthesis of Information-bearing Peptoids and their Sequence-directed Dynamic Covalent Self-assembly

Published on: February 6, 2020

Area of Science:

Cheminformatics
Computational Chemistry
Artificial Intelligence

Background:

Simplified Molecular-Input Line-Entry System (SMILES) is widely used for chemical structure representation in AI.
SMILES has limitations in representing complex macromolecules.
BigSMILES was proposed as an alternative for macromolecules but requires significant preprocessing.

Purpose of the Study:

To develop an automated conversion workflow from SMILES to BigSMILES for homopolymers.
To provide a large dataset of BigSMILES representations for immediate research use.
To validate the accuracy, interchangeability, and robustness of the generated BigSMILES.

Main Methods:

Developed an automated conversion workflow.
Generated BigSMILES representations for over 4.9 million homopolymer records.
Implemented a rigorous validation process for the generated data.
Documented utilized codes and functions for BigSMILES generation.

Main Results:

Successfully generated 4,927,181 BigSMILES representations from SMILES.
Validated the accuracy, interchangeability, and robustness of the BigSMILES conversion.
Provided a comprehensive overview of the generation methodology.

Conclusions:

The automated BigSMILES conversion workflow significantly aids researchers.
This advancement facilitates further BigSMILES studies, including deep learning applications.
The generated dataset enables immediate use in research and development for macromolecule representation.