Development of Quantitative Structure–Anti-Inflammatory Relationships of Alkaloids
Alkaloids are naturally occurring metabolites with a wide variety of pharmacological activities and applications in science, particularly in medicinal chemistry as anti-inflammatory drugs. Because they can be labelled as active or inactive compounds against the inflammatory biological response, the...
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2024-11-01
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| author | Cristian Rojas Doménica Muñoz Ivanna Cordero Belén Tenesaca Davide Ballabio |
| author_facet | Cristian Rojas Doménica Muñoz Ivanna Cordero Belén Tenesaca Davide Ballabio |
| author_sort | Cristian Rojas |
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| description | Alkaloids are naturally occurring metabolites with a wide variety of pharmacological activities and applications in science, particularly in medicinal chemistry as anti-inflammatory drugs. Because they can be labelled as active or inactive compounds against the inflammatory biological response, the aim of this work was to calibrate quantitative structure-activity relationships (QSARs) using machine learning classifiers to predict anti-inflammatory activity based on the molecular structures of alkaloids. A dataset of 100 alkaloids (58 active and 42 inactive) was retrieved from two systematic reviews. Molecules were properly curated, and the molecular geometries of the compounds were optimized using the semi-empirical method (PM3) to calculate molecular descriptors, binary fingerprints (extended-connectivity fingerprints and path fingerprints) and MACCS (Molecular ACCess System) structural keys. Then, we calibrated the QSAR models using well-known linear and non-linear machine learning classifiers, i.e., partial least squares discriminant analysis (PLSDA), random forests (RF), adaptive boosting (AdaBoost), <i>k</i>-nearest neighbors (<i>k</i>NN), <i>N</i>-nearest neighbors (N3) and binned nearest neighbors (BNN). For validation purposes, the dataset was randomly split into a training set and a test set in a 70:30 ratio. When using molecular descriptors, genetic algorithms-variable subset selection (GAs-VSS) was used for supervised feature selection. During the calibration of the models, a five-fold Venetian blinds cross-validation was used to optimize the classifier parameters and to control the presence of overfitting. The performance of the models was quantified by means of the non-error rate (<i>NER</i>) statistical parameter. |
| format | Article |
| id | doaj-art-71fbdedd70f1413a84fd1f0898d42340 |
| institution | Kabale University |
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| language | English |
| publishDate | 2024-11-01 |
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| series | Chemistry Proceedings |
| spelling | doaj-art-71fbdedd70f1413a84fd1f0898d423402025-08-20T03:26:10ZengMDPI AGChemistry Proceedings2673-45832024-11-011617710.3390/ecsoc-28-20159Development of Quantitative Structure–Anti-Inflammatory Relationships of AlkaloidsCristian Rojas0Doménica Muñoz1Ivanna Cordero2Belén Tenesaca3Davide Ballabio4Grupo de Investigación en Quimiometría y QSAR, Facultad de Ciencia y Tecnología, Universidad del Azuay, Av. 24 de Mayo 7-77 y Hernán Malo, Cuenca 010107, EcuadorUnidad Académica de Salud y Bienestar, Universidad Católica de Cuenca, Av. de las Américas y Humboldt, Cuenca 010101, EcuadorFacultad de Medicina, Universidad del Azuay, Av. 24 de Mayo 7-77 y Hernán Malo, Cuenca 010107, EcuadorFacultad de Medicina, Universidad del Azuay, Av. 24 de Mayo 7-77 y Hernán Malo, Cuenca 010107, EcuadorMilano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, P.za della Scienza 1, 20126 Milano, ItalyAlkaloids are naturally occurring metabolites with a wide variety of pharmacological activities and applications in science, particularly in medicinal chemistry as anti-inflammatory drugs. Because they can be labelled as active or inactive compounds against the inflammatory biological response, the aim of this work was to calibrate quantitative structure-activity relationships (QSARs) using machine learning classifiers to predict anti-inflammatory activity based on the molecular structures of alkaloids. A dataset of 100 alkaloids (58 active and 42 inactive) was retrieved from two systematic reviews. Molecules were properly curated, and the molecular geometries of the compounds were optimized using the semi-empirical method (PM3) to calculate molecular descriptors, binary fingerprints (extended-connectivity fingerprints and path fingerprints) and MACCS (Molecular ACCess System) structural keys. Then, we calibrated the QSAR models using well-known linear and non-linear machine learning classifiers, i.e., partial least squares discriminant analysis (PLSDA), random forests (RF), adaptive boosting (AdaBoost), <i>k</i>-nearest neighbors (<i>k</i>NN), <i>N</i>-nearest neighbors (N3) and binned nearest neighbors (BNN). For validation purposes, the dataset was randomly split into a training set and a test set in a 70:30 ratio. When using molecular descriptors, genetic algorithms-variable subset selection (GAs-VSS) was used for supervised feature selection. During the calibration of the models, a five-fold Venetian blinds cross-validation was used to optimize the classifier parameters and to control the presence of overfitting. The performance of the models was quantified by means of the non-error rate (<i>NER</i>) statistical parameter.https://www.mdpi.com/2673-4583/16/1/77alkaloidsanti-inflammatory activitymolecular descriptorsmachine learning classifiersQSAR |
| spellingShingle | Cristian Rojas Doménica Muñoz Ivanna Cordero Belén Tenesaca Davide Ballabio Development of Quantitative Structure–Anti-Inflammatory Relationships of Alkaloids Chemistry Proceedings alkaloids anti-inflammatory activity molecular descriptors machine learning classifiers QSAR |
| title | Development of Quantitative Structure–Anti-Inflammatory Relationships of Alkaloids |
| title_full | Development of Quantitative Structure–Anti-Inflammatory Relationships of Alkaloids |
| title_fullStr | Development of Quantitative Structure–Anti-Inflammatory Relationships of Alkaloids |
| title_full_unstemmed | Development of Quantitative Structure–Anti-Inflammatory Relationships of Alkaloids |
| title_short | Development of Quantitative Structure–Anti-Inflammatory Relationships of Alkaloids |
| title_sort | development of quantitative structure anti inflammatory relationships of alkaloids |
| topic | alkaloids anti-inflammatory activity molecular descriptors machine learning classifiers QSAR |
| url | https://www.mdpi.com/2673-4583/16/1/77 |
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