The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
BigSMILES: A Structurally-Based Line Notation for Describing Macromolecules
Open accessAbstract
Having a compact yet robust structurally based identifier or representation system is a key enabling factor for efficient sharing and dissemination of research results within the chemistry community, and such systems lay down the essential foundations for future informatics and data-driven research. While substantial advances have been made for small molecules, the polymer community has struggled in coming up with an efficient representation system. This is because, unlike other disciplines in chemistry, the basic premise that each distinct chemical species corresponds to a well-defined chemical structure does not hold for polymers. Polymers are intrinsically stochastic molecules that are often ensembles with a distribution of chemical structures. This difficulty limits the applicability of all deterministic representations developed for small molecules. In this work, a new representation system that is capable of handling the stochastic nature of polymers is proposed. The new system is based on the popular "simplified molecular-input line-entry system" (SMILES), and it aims to provide representations that can be used as indexing identifiers for entries in polymer databases. As a pilot test, the entries of the standard data set of the glass transition temperature of linear polymers (Bicerano, 2002) were converted into the new BigSMILES language. Furthermore, it is hoped that the proposed system will provide a more effective language for communication within the polymer community and increase cohesion between the researchers within the community.
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Cites
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Cited by
Scientific discovery in the age of artificial intelligence.
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“A SMILES string is a simple gridlike (1-D grid) structure that can represent molecular sequences such as DNA, macromolecules/polymers, protein sequences also [140, 141].”
BackgroundMolecular representations in AI-driven drug discovery: a review and practical guide
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Self-citationMachine Learning on a Robotic Platform for the Design of Polymer–Protein Hybrids
Tamasi, M. J., Patel, R. A., Borca, C. H., Kosuri, S., Mugnier, H., Upadhya, R., Murthy, N. S., Webb, M. A., & Gormley, A. J. (2022) · Advanced Materials · doi:10.1002/adma.202201809 · Cited by 177
Metadata
- Authors
- Lin, T.-S., Coley, C. W., Mochigase, H., Beech, H. K., Wang, W., Wang, Z., Woods, E., Craig, S. L., Johnson, J. A., Kalow, J. A., Jensen, K. F., & Olsen, B. D.
- Venue
- ACS Central Science
- Publisher
- American Chemical Society (ACS)
- Published
- 2019-09-12
- Volume
- 5
- Issue
- 9
- Pages
- 1523-1531
- ISSN
- 2374-7943 (ISSN-L)
- 2374-7951
- OpenAlex
- W2973074478
- Cited by
- 322
- References
- 35
- FWCI
- 8.21
- Type
- journal article
- Language
- en
- Open access
- diamond
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- Lin, T.-S., Coley, C. W., Mochigase, H., Beech, H. K., Wang, W., Wang, Z., Woods, E., Craig, S. L., Johnson, J. A., Kalow, J. A., Jensen, K. F., & Olsen, B. D. (2019). BigSMILES: A Structurally-Based Line Notation for Describing Macromolecules. ACS Central Science. https://doi.org/10.1021/acscentsci.9b00476
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