QSAR analysis on Spodoptera litura antifeedant activities for flavone derivatives

Duchowicz, Pablo R.; Goodarzi, Mohammad; Ocsachoque, Marco Antonio; Romanelli, Gustavo Pablo; Ortiz, Erlinda V.; Autino, Juan Carlos; Bennardi, Daniel Oscar; Ruiz, Diego Manuel; Castro, Eduardo A.

doi:10.1016/j.scitotenv.2009.09.041

Cited by 25 publications

(16 citation statements)

References 24 publications

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“…The quality of the results achieved with this technique is quite close to that obtained by performing an exact (combinatorial) full search (FS) of molecular descriptors, although, of course, it requires much less computational work (45). In addition, the RM provides models with statistical parameters better than those from the forward stepwise regression procedure and similar to those from the more elaborated genetic algorithms approach (36,37,46,47). All the models presented here were properly validated.…”

Section: Evaluation Test Of Compoundssupporting

confidence: 56%

Sustainable Synthesis of Flavonoid Derivatives, QSAR Study and Insecticidal Activity against the Fall Armyworm, Spodoptera frugiperda (Lep.: Noctuidae)

Romanelli¹,

Virla²,

Duchowicz³

et al. 2010

J. Agric. Food Chem.

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A simple, clean, solvent-free preparation of flavones by the use of a silica-supported Preyssler heteropolyacid as reusable catalyst is described. High selectivity and very good yields (87-94%) were obtained in short reaction times (7-13 min). Bioassays for insecticidal activity against Spodoptera frugiperda were carried out with a set of flavones. Bioassays showed that some of the flavones had moderate insecticidal activity. Quantitative structure-activity relationships were established on the available data with the purpose of predicting the insecticidal activity of a number of structurally related flavones. A relationship between the molecular structure and biological activity is proposed.

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Section: Evaluation Test Of Compoundssupporting

confidence: 56%

Sustainable Synthesis of Flavonoid Derivatives, QSAR Study and Insecticidal Activity against the Fall Armyworm, Spodoptera frugiperda (Lep.: Noctuidae)

Romanelli¹,

Virla²,

Duchowicz³

et al. 2010

J. Agric. Food Chem.

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“…The obtained results indicated the proper ability of RM as feature selection approach. RM gave models with better statistical parameters (correlation coefficient as well as internal and external validation) than the stepwise regression procedure and similar or better ones than the more elaborated GA (86)(87)(88)(89)(90).…”

Section: Wrapper Methodsmentioning

confidence: 92%

“…This process is repeated as many times as necessary until the set of features remains unchanged. This method has been used on different classes of compounds (86)(87)(88)(89)(90), such as on the antifeedant activities of aurones, chromones, and flavones; the %HIA (human internal absorption) permeability values of 160 organic compounds; 154 non-nucleoside reverse transcriptase inhibitors (NNRTI) of the wild-type HIV-1 virus; the antituberculosis activity of 43 quinoxaline-2-carboxylate 1,4-di-N-oxide derivatives; and the binding affinity constants of flavonoid ligands for the benzodiazepine site of the gammaaminobutyric acid (GABA)(A) receptor complex. The obtained results indicated the proper ability of RM as feature selection approach.…”

Section: Wrapper Methodsmentioning

confidence: 99%

Feature Selection Methods in QSAR Studies

Goodarzi

Dejaegher

Heyden

2012

Journal of AOAC INTERNATIONAL

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120

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A quantitative structure-activity relationship (QSAR) relates quantitative chemical structure attributes (molecular descriptors) to a biological activity. QSAR studies have now become attractive in drug discovery and development because their application can save substantial time and human resources. Several parameters are important in the prediction ability of a QSAR model. On the one hand, different statistical methods may be applied to check the linear or nonlinear behavior of a data set. On the other hand, feature selection techniques are applied to decrease the model complexity, to decrease the overfitting/overtraining risk, and to select the most important descriptors from the often more than 1000 calculated. The selected descriptors are then linked to a biological activity of the corresponding compound by means of a mathematical model. Different modeling techniques can be applied, some of which explicitly require a feature selection. A QSAR model can be useful in the design of new compounds with improved potency in the class under study. Only molecules with a predicted interesting activity will be synthesized. In the feature selection problem, a learning algorithm is faced with the problem of selecting a relevant subset of features upon which to focus attention, while ignoring the rest. Up to now, many feature selection techniques, such as genetic algorithms, forward selection, backward elimination, stepwise regression, and simulated annealing have been used extensively. Swarm intelligence optimizations, such as ant colony optimization and partial swarm optimization, which are feature selection techniques usually simulated based on animal and insect life behavior to find the shortest path between a food source and their nests, recently are also involved in QSAR studies. This review paper provides an overview of different feature selection techniques applied in QSAR modeling.

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“…RM and ERM have been utilized with satisfactory results in many QSAR/QSPR reports [116][117][118][119][120][121][122][123].…”

Section: Replacement Methods (Rm)mentioning

confidence: 99%

Chemometrics tools in QSAR/QSPR studies: A historical perspective

Yousefinejad

Hemmateenejad

2015

Chemometrics and Intelligent Laboratory Systems

119

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QSAR analysis on Spodoptera litura antifeedant activities for flavone derivatives

Cited by 25 publications

References 24 publications

Sustainable Synthesis of Flavonoid Derivatives, QSAR Study and Insecticidal Activity against the Fall Armyworm, Spodoptera frugiperda (Lep.: Noctuidae)

Sustainable Synthesis of Flavonoid Derivatives, QSAR Study and Insecticidal Activity against the Fall Armyworm, Spodoptera frugiperda (Lep.: Noctuidae)

Feature Selection Methods in QSAR Studies

Chemometrics tools in QSAR/QSPR studies: A historical perspective

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