Extending BigSMILES to non-covalent bonds in supramolecular polymer assemblies. Issue 41 (4th October 2022)

• Record Type: Journal Article
• Authors: Zou, Weizhong; Martell Monterroza, Alexis; Yao, Yunxin; Millik, S. Cem; Cencer, Morgan M.; Rebello, Nathan J.; Beech, Haley K.; Morris, Melody A.; Lin, Tzyy-Shyang; Castano, Cleotilde S.; Kalow, Julia A.; Craig, Stephen L.; Nelson, Alshakim; Moore, Jeffrey S.; Olsen, Bradley D.
• Is Part Of: Chemical science. Volume 13:Issue 41(2022)
• Page Start: 12045
• Page End: 12055
• Publication Date: 2022-10-04
• Subjects: Chemistry -- Periodicals; 540.5
• DOI: 10.1039/d2sc02257e
• Languages: English
• ISSNs: 2041-6520

Abstract

Abstract : Non-covalent BigSMILES enables the representation of donor/acceptor interactions and delocalized bonds for polymer assemblies. Abstract : As a machine-recognizable representation of polymer connectivity, BigSMILES line notation extends SMILES from deterministic to stochastic structures. The same framework that allows BigSMILES to accommodate stochastic covalent connectivity can be extended to non-covalent bonds, enhancing its value for polymers, supramolecular materials, and colloidal chemistry. Non-covalent bonds are captured through the inclusion of annotations to pseudo atoms serving as complementary binding pairs, minimal key/value pairs to elaborate other relevant attributes, and indexes to specify the pairing among potential donors and acceptors or bond delocalization. Incorporating these annotations into BigSMILES line notation enables the representation of four common classes of non-covalent bonds in polymer science: electrostatic interactions, hydrogen bonding, metal–ligand complexation, and π–π stacking. The principal advantage of non-covalent BigSMILES is the ability to accommodate a broad variety of non-covalent chemistry with a simple user-orientated, semi-flexible annotation formalism. This goal is achieved by encoding a universal but non-exhaustive representation of non-covalent or stochastic bonding patterns through syntax for (de)protonated and delocalized state of bonding as well as nested bonds for correlated bonding and multi-component mixture. By allowing user-defined descriptors in the annotation expression, further applications in data-driven research can be envisioned to represent chemical structures in many other fields, including polymer nanocomposite and surface chemistry.