| Full text | |
| Author(s): |
Total Authors: 4
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| Affiliation: | [1] Univ Campinas UNICAMP, Inst Chem, BR-13083970 Campinas, SP - Brazil
[2] Univ Campinas UNICAMP, Inst Language Studies, BR-13083970 Campinas, SP - Brazil
Total Affiliations: 2
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| Document type: | Journal article |
| Source: | MATHEMATICS; v. 9, n. 1 JAN 2021. |
| Web of Science Citations: | 0 |
| Abstract | |
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-2 values. We also present the software Charming QSAR \& QSPR, written in Python, for the property prediction of chemical compounds while using this approach. (AU) | |
| FAPESP's process: | 16/10498-4 - Investigation of strategies of adaptation to the pathogenic life style of fungi from the Moniliophthora genus at various levels of biological organizations: species, biotypes, and geographic lineages |
| Grantee: | Antonio Vargas de Oliveira Figueira |
| Support Opportunities: | Research Projects - Thematic Grants |