Chemistry-informed macromolecule graph representation for similarity computation, unsupervised and supervised learning
Name
Mohapatra_2022_Mach._Learn. _Sci._Technol._3_015028.pdf
Description
Published version
Size
8.26 MB
Format
Adobe PDF
Checksum (MD5)
e4e4367781016b6f09495b1232750374
Author(s)
Mohapatra, Somesh
An, Joyce
Gómez-Bombarelli, Rafael
Date Issued
March 1, 2022
Journal
Machine Learning: Science and Technology
Publisher
IOP Publishing
Citation
Mohapatra, Somesh, An, Joyce and Gómez-Bombarelli, Rafael. 2022. "Chemistry-informed macromolecule graph representation for similarity computation, unsupervised and supervised learning." Machine Learning: Science and Technology, 3 (1).
Version
Final published version
Abstract
Abstract
The near-infinite chemical diversity of natural and artificial macromolecules arises from the vast range of possible component monomers, linkages, and polymers topologies. This enormous variety contributes to the ubiquity and indispensability of macromolecules but hinders the development of general machine learning methods with macromolecules as input. To address this, we developed a chemistry-informed graph representation of macromolecules that enables quantifying structural similarity, and interpretable supervised learning for macromolecules. Our work enables quantitative chemistry-informed decision-making and iterative design in the macromolecular chemical space.
The near-infinite chemical diversity of natural and artificial macromolecules arises from the vast range of possible component monomers, linkages, and polymers topologies. This enormous variety contributes to the ubiquity and indispensability of macromolecules but hinders the development of general machine learning methods with macromolecules as input. To address this, we developed a chemistry-informed graph representation of macromolecules that enables quantifying structural similarity, and interpretable supervised learning for macromolecules. Our work enables quantitative chemistry-informed decision-making and iterative design in the macromolecular chemical space.
MIT Department
Massachusetts Institute of Technology. Department of Materials Science and Engineering
Terms of Use
Creative Commons Attribution 4.0 International License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1088/2632-2153/ac545e
