3D-QSARpy: Combining variable selection strategies and machine learning techniques to build QSAR models
Abstract Quantitative Structure-Activity Relationship (QSAR) is a computer-aided technology in the field of medicinal chemistry that seeks to clarify the relationships between molecular structures and their biological activities. Such technologies allow for the acceleration of the development of new...
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| Format: | Article |
| Language: | English |
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Universidade de São Paulo
2023-05-01
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| Series: | Brazilian Journal of Pharmaceutical Sciences |
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| Online Access: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1984-82502023000100358&lng=en&tlng=en |
| _version_ | 1797826247098630144 |
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| author | Priscilla Suene de Santana Nogueira Silverio Jéssika de Oliveira Viana Euzébio Guimarães Barbosa |
| author_facet | Priscilla Suene de Santana Nogueira Silverio Jéssika de Oliveira Viana Euzébio Guimarães Barbosa |
| author_sort | Priscilla Suene de Santana Nogueira Silverio |
| collection | DOAJ |
| description | Abstract Quantitative Structure-Activity Relationship (QSAR) is a computer-aided technology in the field of medicinal chemistry that seeks to clarify the relationships between molecular structures and their biological activities. Such technologies allow for the acceleration of the development of new compounds by reducing the costs of drug design. This work presents 3D-QSARpy, a flexible, user-friendly and robust tool, freely available without registration, to support the generation of QSAR 3D models in an automated way. The user only needs to provide aligned molecular structures and the respective dependent variable. The current version was developed using Python with packages such as scikit-learn and includes various techniques of machine learning for regression. The diverse techniques employed by the tool is a differential compared to known methodologies, such as CoMFA and CoMSIA, because it expands the search space of possible solutions, and in this way increases the chances of obtaining relevant models. Additionally, approaches for select variables (dimension reduction) were implemented in the tool. To evaluate its potentials, experiments were carried out to compare results obtained from the proposed 3D-QSARpy tool with the results from already published works. The results demonstrated that 3D-QSARpy is extremely useful in the field due to its expressive results. |
| first_indexed | 2024-03-13T11:05:55Z |
| format | Article |
| id | doaj.art-d9f68ffb89ad4dfba7d81306acde55cb |
| institution | Directory Open Access Journal |
| issn | 2175-9790 |
| language | English |
| last_indexed | 2024-03-13T11:05:55Z |
| publishDate | 2023-05-01 |
| publisher | Universidade de São Paulo |
| record_format | Article |
| series | Brazilian Journal of Pharmaceutical Sciences |
| spelling | doaj.art-d9f68ffb89ad4dfba7d81306acde55cb2023-05-16T07:34:15ZengUniversidade de São PauloBrazilian Journal of Pharmaceutical Sciences2175-97902023-05-015910.1590/s2175-97902023e223733D-QSARpy: Combining variable selection strategies and machine learning techniques to build QSAR modelsPriscilla Suene de Santana Nogueira SilverioJéssika de Oliveira VianaEuzébio Guimarães Barbosahttps://orcid.org/0000-0002-7685-9618Abstract Quantitative Structure-Activity Relationship (QSAR) is a computer-aided technology in the field of medicinal chemistry that seeks to clarify the relationships between molecular structures and their biological activities. Such technologies allow for the acceleration of the development of new compounds by reducing the costs of drug design. This work presents 3D-QSARpy, a flexible, user-friendly and robust tool, freely available without registration, to support the generation of QSAR 3D models in an automated way. The user only needs to provide aligned molecular structures and the respective dependent variable. The current version was developed using Python with packages such as scikit-learn and includes various techniques of machine learning for regression. The diverse techniques employed by the tool is a differential compared to known methodologies, such as CoMFA and CoMSIA, because it expands the search space of possible solutions, and in this way increases the chances of obtaining relevant models. Additionally, approaches for select variables (dimension reduction) were implemented in the tool. To evaluate its potentials, experiments were carried out to compare results obtained from the proposed 3D-QSARpy tool with the results from already published works. The results demonstrated that 3D-QSARpy is extremely useful in the field due to its expressive results.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1984-82502023000100358&lng=en&tlng=enDrug Design3D-QSARMachine learningVariable selection |
| spellingShingle | Priscilla Suene de Santana Nogueira Silverio Jéssika de Oliveira Viana Euzébio Guimarães Barbosa 3D-QSARpy: Combining variable selection strategies and machine learning techniques to build QSAR models Brazilian Journal of Pharmaceutical Sciences Drug Design 3D-QSAR Machine learning Variable selection |
| title | 3D-QSARpy: Combining variable selection strategies and machine learning techniques to build QSAR models |
| title_full | 3D-QSARpy: Combining variable selection strategies and machine learning techniques to build QSAR models |
| title_fullStr | 3D-QSARpy: Combining variable selection strategies and machine learning techniques to build QSAR models |
| title_full_unstemmed | 3D-QSARpy: Combining variable selection strategies and machine learning techniques to build QSAR models |
| title_short | 3D-QSARpy: Combining variable selection strategies and machine learning techniques to build QSAR models |
| title_sort | 3d qsarpy combining variable selection strategies and machine learning techniques to build qsar models |
| topic | Drug Design 3D-QSAR Machine learning Variable selection |
| url | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1984-82502023000100358&lng=en&tlng=en |
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