Chemistry-informed macromolecule graph representation for similarity computation, unsupervised and supervised learning
<jats:title>Abstract</jats:title> <jats:p>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 an...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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IOP Publishing
2022
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| Online Access: | https://hdl.handle.net/1721.1/142530 |
| _version_ | 1810988308370554880 |
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| author | Mohapatra, Somesh An, Joyce Gómez-Bombarelli, Rafael |
| author2 | Massachusetts Institute of Technology. Department of Materials Science and Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Materials Science and Engineering Mohapatra, Somesh An, Joyce Gómez-Bombarelli, Rafael |
| author_sort | Mohapatra, Somesh |
| collection | MIT |
| description | <jats:title>Abstract</jats:title>
<jats:p>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.</jats:p> |
| first_indexed | 2024-09-23T12:03:56Z |
| format | Article |
| id | mit-1721.1/142530 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T12:03:56Z |
| publishDate | 2022 |
| publisher | IOP Publishing |
| record_format | dspace |
| spelling | mit-1721.1/1425302023-02-06T20:21:05Z Chemistry-informed macromolecule graph representation for similarity computation, unsupervised and supervised learning Mohapatra, Somesh An, Joyce Gómez-Bombarelli, Rafael Massachusetts Institute of Technology. Department of Materials Science and Engineering <jats:title>Abstract</jats:title> <jats:p>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.</jats:p> 2022-05-13T15:59:43Z 2022-05-13T15:59:43Z 2022-03-01 2022-05-13T15:55:32Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/142530 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). en 10.1088/2632-2153/ac545e Machine Learning: Science and Technology Creative Commons Attribution 4.0 International License https://creativecommons.org/licenses/by/4.0 application/pdf IOP Publishing IOP Publishing |
| spellingShingle | Mohapatra, Somesh An, Joyce Gómez-Bombarelli, Rafael Chemistry-informed macromolecule graph representation for similarity computation, unsupervised and supervised learning |
| title | Chemistry-informed macromolecule graph representation for similarity computation, unsupervised and supervised learning |
| title_full | Chemistry-informed macromolecule graph representation for similarity computation, unsupervised and supervised learning |
| title_fullStr | Chemistry-informed macromolecule graph representation for similarity computation, unsupervised and supervised learning |
| title_full_unstemmed | Chemistry-informed macromolecule graph representation for similarity computation, unsupervised and supervised learning |
| title_short | Chemistry-informed macromolecule graph representation for similarity computation, unsupervised and supervised learning |
| title_sort | chemistry informed macromolecule graph representation for similarity computation unsupervised and supervised learning |
| url | https://hdl.handle.net/1721.1/142530 |
| work_keys_str_mv | AT mohapatrasomesh chemistryinformedmacromoleculegraphrepresentationforsimilaritycomputationunsupervisedandsupervisedlearning AT anjoyce chemistryinformedmacromoleculegraphrepresentationforsimilaritycomputationunsupervisedandsupervisedlearning AT gomezbombarellirafael chemistryinformedmacromoleculegraphrepresentationforsimilaritycomputationunsupervisedandsupervisedlearning |