Chemical Graph Theory for Property Modeling in QSAR and QSPR—Charming QSAR & QSPR
Copyright policy )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) 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 R2 values. We also present the software Charming QSAR & QSPR, written in Python, for the property prediction of chemical compounds while using this approach.
- State University of Campinas Brazil
Artificial intelligence, Computational chemistry, Materials Science, gradient boosting machine, Graph, Computational Chemistry, Theoretical computer science, Machine learning, QA1-939, Materials Chemistry, FOS: Mathematics, Environmental Chemistry, support vector machine, QSAR Modeling, Biology, Accelerating Materials Innovation through Informatics, fragment based QSAR, Computer science, Chemistry, fragment based QSPR, Computational Theory and Mathematics, Chemical Properties, Biological system, Computer Science, Physical Sciences, Environmental Science, Principles and Applications of Green Chemistry, Quantitative structure–activity relationship, random forest, Molecular graph, Mathematics, Computational Methods in Drug Discovery, Molecular descriptor
Artificial intelligence, Computational chemistry, Materials Science, gradient boosting machine, Graph, Computational Chemistry, Theoretical computer science, Machine learning, QA1-939, Materials Chemistry, FOS: Mathematics, Environmental Chemistry, support vector machine, QSAR Modeling, Biology, Accelerating Materials Innovation through Informatics, fragment based QSAR, Computer science, Chemistry, fragment based QSPR, Computational Theory and Mathematics, Chemical Properties, Biological system, Computer Science, Physical Sciences, Environmental Science, Principles and Applications of Green Chemistry, Quantitative structure–activity relationship, random forest, Molecular graph, Mathematics, Computational Methods in Drug Discovery, Molecular descriptor
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