Chemical Graph Theory for Property Modeling in QSAR and QSPR—Charming QSAR & QSPR
Quantitative structure-activity relationship (QSAR) and Quantitative structure-property relationship (QSPR) are mathematical models for the prediction of the chemical, physical or biological properties of chemical compounds. Usually, they are based on structural (grounded on fragment contribution) o...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-12-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/9/1/60 |
| _version_ | 1797543143249281024 |
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| author | Paulo C. S. Costa Joel S. Evangelista Igor Leal Paulo C. M. L. Miranda |
| author_facet | Paulo C. S. Costa Joel S. Evangelista Igor Leal Paulo C. M. L. Miranda |
| author_sort | Paulo C. S. Costa |
| collection | DOAJ |
| description | Quantitative structure-activity relationship (QSAR) and Quantitative structure-property relationship (QSPR) are mathematical models for the prediction of the chemical, physical or biological properties of chemical compounds. Usually, they are based on structural (grounded on fragment contribution) or calculated (centered on QSAR three-dimensional (QSAR-3D) or chemical descriptors) parameters. Hereby, we describe a Graph Theory approach for generating and mining molecular fragments to be used in QSAR or QSPR modeling based exclusively on fragment contributions. Merging of Molecular Graph Theory, Simplified Molecular Input Line Entry Specification (SMILES) notation, and the connection table data allows a precise way to differentiate and count the molecular fragments. Machine learning strategies generated models with outstanding root mean square error (RMSE) and R<sup>2</sup> values. We also present the software <i>Charming QSAR & QSPR</i>, written in Python, for the property prediction of chemical compounds while using this approach. |
| first_indexed | 2024-03-10T13:40:31Z |
| format | Article |
| id | doaj.art-429894e28e8e4df2bdfab0c39e754fef |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T13:40:31Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-429894e28e8e4df2bdfab0c39e754fef2023-11-21T03:01:43ZengMDPI AGMathematics2227-73902020-12-01916010.3390/math9010060Chemical Graph Theory for Property Modeling in QSAR and QSPR—Charming QSAR & QSPRPaulo C. S. Costa0Joel S. Evangelista1Igor Leal2Paulo C. M. L. Miranda3Institute of Chemistry, University of Campinas—UNICAMP, Campinas, SP 13083-970, BrazilInstitute of Chemistry, University of Campinas—UNICAMP, Campinas, SP 13083-970, BrazilInstitute of Language Studies, University of Campinas—UNICAMP, Campinas, SP 13083-970, BrazilInstitute of Chemistry, University of Campinas—UNICAMP, Campinas, SP 13083-970, BrazilQuantitative structure-activity relationship (QSAR) and Quantitative structure-property relationship (QSPR) are mathematical models for the prediction of the chemical, physical or biological properties of chemical compounds. Usually, they are based on structural (grounded on fragment contribution) or calculated (centered on QSAR three-dimensional (QSAR-3D) or chemical descriptors) parameters. Hereby, we describe a Graph Theory approach for generating and mining molecular fragments to be used in QSAR or QSPR modeling based exclusively on fragment contributions. Merging of Molecular Graph Theory, Simplified Molecular Input Line Entry Specification (SMILES) notation, and the connection table data allows a precise way to differentiate and count the molecular fragments. Machine learning strategies generated models with outstanding root mean square error (RMSE) and R<sup>2</sup> values. We also present the software <i>Charming QSAR & QSPR</i>, written in Python, for the property prediction of chemical compounds while using this approach.https://www.mdpi.com/2227-7390/9/1/60fragment based QSARfragment based QSPRsupport vector machinerandom forestgradient boosting machine |
| spellingShingle | Paulo C. S. Costa Joel S. Evangelista Igor Leal Paulo C. M. L. Miranda Chemical Graph Theory for Property Modeling in QSAR and QSPR—Charming QSAR & QSPR Mathematics fragment based QSAR fragment based QSPR support vector machine random forest gradient boosting machine |
| title | Chemical Graph Theory for Property Modeling in QSAR and QSPR—Charming QSAR & QSPR |
| title_full | Chemical Graph Theory for Property Modeling in QSAR and QSPR—Charming QSAR & QSPR |
| title_fullStr | Chemical Graph Theory for Property Modeling in QSAR and QSPR—Charming QSAR & QSPR |
| title_full_unstemmed | Chemical Graph Theory for Property Modeling in QSAR and QSPR—Charming QSAR & QSPR |
| title_short | Chemical Graph Theory for Property Modeling in QSAR and QSPR—Charming QSAR & QSPR |
| title_sort | chemical graph theory for property modeling in qsar and qspr charming qsar qspr |
| topic | fragment based QSAR fragment based QSPR support vector machine random forest gradient boosting machine |
| url | https://www.mdpi.com/2227-7390/9/1/60 |
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