A non-conformational QSAR study for plant-derived larvicides against Zika Aedes aegypti L. vector
- 1. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas (INIFTA), CONICET, UNLP (Argentina)
- 2. Universidad Nacional de La Plata. Cátedra de Química Orgánica, Centro de Investigación en Sanidad Vegetal (CISaV), Facultad de Ciencias Agrarias y Forestales (Argentina)
- 3. CONICET, UNLP, Centro de Investigación y Desarrollo en Ciencias Aplicadas "Dr. J.J. Ronco" (CINDECA). Departamento de Química, Facultad de Ciencias Exactas (Argentina)
Description
A set of 263 plant-derived compounds with larvicidal activity against Aedes aegypti L. (Diptera: Culicidae) vector is collected from the literature, and is studied by means of a non-conformational quantitative structure-activity relationships (QSAR) approach. The balanced subsets method (BSM) is employed to split the complete dataset into training, validation and test sets. From 26,775 freely available molecular descriptors, the most relevant structural features of compounds affecting the bioactivity are taken. The molecular descriptors are calculated through four different freewares, such as PaDEL, Mold2, EPI Suite and QuBiLs-MAS. The replacement method (RM) variable subset selection technique leads to the best linear regression models. A successful QSAR equation involves 7-conformation-independent molecular descriptors, fulfiling the evaluated internal (loo, l30%o, VIF and Y-randomization) and external (test set with Ntest = 65 compounds) validation criteria. The practical application of this QSAR model reveals promising predicted values for some natural compounds with unknown experimental larvicidal activity. Therefore, the present model constitutes the first one based on a large molecular set, being a useful computational tool for identifying and guiding the synthesis of new active molecules inspired by natural products.
Additional details
Identifiers
Publishing Information
- Journal Title
- Environmental Science and Pollution Research International
- Journal Volume
- 27
- Journal Issue
- 6
- Journal Page Range
- p. 6205-6214
- ISSN
- 0944-1344
- CODEN
- ESPLEC
INIS
- Country of Publication
- Germany
- Country of Input or Organization
- International Atomic Energy Agency (IAEA)
- INIS RN
- 55075582
- Subject category
- S97: MATHEMATICAL METHODS AND COMPUTING; S37: INORGANIC, ORGANIC, PHYSICAL AND ANALYTICAL CHEMISTRY;
- Descriptors DEI
- CLUSTER ANALYSIS; EQUATIONS; MOLECULAR STRUCTURE; MOLECULES; PLANTS; RANDOMNESS; REGRESSION ANALYSIS; STRUCTURE-ACTIVITY RELATIONSHIPS; SYNTHESIS; TRAINING; VALIDATION; VECTORS; VERIFICATION
- Descriptors DEC
- DATA ANALYSIS; DATA PROCESSING; EDUCATION; MATHEMATICS; PROCESSING; STATISTICS; TENSORS; TESTING
Optional Information
- Copyright
- Copyright (c) 2019 © Springer-Verlag GmbH Germany, part of Springer Nature 2019