3D-QSARpy: Combining variable selection strategies and machine learning techniques to build QSAR models
Braz. J. Pharm. Sci. (Online)
;
59: e22373, 2023. tab, graf
Artículo
en Inglés
| LILACS
| ID: biblio-1439538
ABSTRACT
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.
Texto completo:
Disponible
Índice:
LILACS (Américas)
Asunto principal:
Diseño de Fármacos
/
Relación Estructura-Actividad Cuantitativa
/
Aprendizaje Automático
Tipo de estudio:
Evaluación Económica en Salud
/
Estudio pronóstico
Idioma:
Inglés
Revista:
Braz. J. Pharm. Sci. (Online)
Asunto de la revista:
Farmacologia
/
Teraputica
/
Toxicologia
Año:
2023
Tipo del documento:
Artículo
País de afiliación:
Brasil
Institución/País de afiliación:
Federal University of Rio Grande do Norte/BR
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