RESUMEN
OBJECTIVES: Quantitative structure activity relationship (QSAR) was used to study the partition coefficient of some quinolones and their derivatives. METHODS: These molecules are broad-spectrum antibiotic pharmaceutics. First, data were divided into two categories of train and test (validation) sets using a random selection method. Second, three approaches, including stepwise selection (STS) (forward), genetic algorithm (GA), and simulated annealing (SA) were used to select the descriptors, to examine the effect feature selection methods. To find the relation between descriptors and partition coefficient, multiple linear regression (MLR), principal component regression (PCR) and partial least squares (PLS) were used. RESULTS: QSAR study showed that both regression and descriptor selection methods have a vital role in the results. Different statistical metrics showed that the MLR-SA approach with (r2=0.96, q2=0.91, pred_r2=0.95) gives the best outcome. CONCLUSION: The proposed expression by the MLR-SA approach can be used in the better design of novel quinolones and their derivatives.
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Diseño de Fármacos/métodos , Lípidos/química , Relación Estructura-Actividad Cuantitativa , Quinolonas , Solubilidad , Termodinámica , Algoritmos , Antibacterianos/análisis , Antibacterianos/química , Antibacterianos/farmacología , Humanos , Modelos Genéticos , Simulación de Dinámica Molecular , Quinolonas/análisis , Quinolonas/química , Quinolonas/farmacologíaRESUMEN
AIMS: Prediction of oral acute toxicity of organophosphates using QSAR methods. BACKGROUND: Prediction of oral acute toxicity of organophosphates (including some pesticides and insecticides) using GA-MLR and BPANN methods. OBJECTIVE: The aim of the present study was to develop quantitative structure-activity relationship (QSAR) models, based on molecular descriptors to predict the oral acute toxicity (LD50) of organophosphate compounds. METHODS: The QSAR models based on genetic algorithm-multiple linear regression (GA-MLR) and back-propagation artificial neural network (BPANN) methods were proposed. The prediction experiment showed that the BPANN method was a reliable model for screening molecular descriptors, and molecular descriptors obtained by BPANN models could well characterize the molecular structure of each compound. RESULTS: It was indicated that among molecular descriptors to predict the LD50 of organophosphates, ALOGP2, RDF030u, RDF065p and GATS5m descriptors have more importance than the other descriptors. Also BPANN approach with the values of root mean square error (RMSE= 0.00168), square correlation coefficient (R2 = 0.9999) and absolute average deviation (AAD=0.001675045) gave the best outcome, and the model predictions were in good agreement with experimental data. CONCLUSION: The proposed model may be useful for predicting LD50 of new compounds of similar class.
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Redes Neurales de la Computación , Organofosfatos/toxicidad , Plaguicidas/toxicidad , Administración Oral , Algoritmos , Dosificación Letal Mediana , Modelos Lineales , Modelos Moleculares , Organofosfatos/química , Plaguicidas/química , Relación Estructura-Actividad Cuantitativa , Reproducibilidad de los Resultados , Pruebas de Toxicidad AgudaRESUMEN
BACKGROUND: In this study, we used a hierarchical approach to develop quantitative structure-activity relationship (QSAR) models for modeling physico-chemical properties of quinolone derivatives. OBJECTIVE: The relationship between some of the molecular descriptors with physic-chemical properties such as refractive index (n), polarizability (α) and HOMO-LUMO energy gap (ΔEH-L) was represented. MATERIALS AND METHODS: Quantum mechanical calculations using abinitio method at the #HF/6- 31++G** level were carried out to obtain the optimized geometry and then, the comprehensive set of molecular descriptors was computed by using the Dragon software. Genetic algorithm using multiple linear regression (GA-MLR) with backward method by SPSS software were utilized to construct QSAR models. RESULTS: The analytical powers of the established theoretical models were discussed using leaveone- out (LOO) cross-validation technique. A multi-parametric equation containing maximum three descriptors with suitable statistical qualities was obtained for predicting the studied properties. CONCLUSION: The QSPR analysis for the prediction of the refractive index, the polarizability and the HOMO-LUMO energy gap of 40 quinolone derivatives using GA-MLR method was performed. The achieved results showed that the best model for predicting the refractive index, the polarizability and the HOMO-LUMO energy gap contains maximum three descriptors. MLR analysis, using genetic algorithms as suitable descriptors selection method showed that the three selected descriptors play a vital role in the prediction of physicochemical properties of quinolone derivatives. It can be noted that the best descriptors in the final obtained models can be used to design and screen new drugs.
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Simulación por Computador , Quinolonas/química , Algoritmos , Modelos Lineales , Modelos Moleculares , Estructura Molecular , Relación Estructura-Actividad Cuantitativa , Quinolinas , Programas InformáticosRESUMEN
BACKGROUND: In this study, we used a hierarchical approach to develop quantitative structureactivity relationship (QSAR) models for modeling lipophilicity of a set of 81 aniline derivatives containing some pharmaceutical compounds. OBJECTIVE: The multiple linear regression (MLR), principal component regression (PCR) and partial least square regression (PLSR) methods were utilized to construct QSAR models. MATERIALS AND METHODS: Quantum mechanical calculations at the density functional theory level and 6- 311++G** basis set were carried out to obtain the optimized geometry and then, the comprehensive set of molecular descriptors was computed by using the Dragon software. Genetic algorithm (GA) was applied to select suitable descriptors which have the most correlation with lipophilicity of the studied compounds. RESULTS: It was identified that such descriptors as Barysz matrix (SEigZ), hydrophilicity factor (Hy), Moriguchi octanol-water partition coefficient (MLOGP), electrophilicity (ω/eV) van der Waals volume (vWV) and lethal concentration (LC50/molkg-1) are the best descriptors for QSAR modeling. The high correlation coefficients and the low prediction errors for MLR, PCR and PLSR methods confirmed good predictability of the three models. CONCLUSION: In present study, the high correlation between experimental and predicted logP values of aniline derivatives indicated the validation and the good quality of the resulting three regression methods, but MLR regression procedure was a little better than the PCR and PLSR methods. It was concluded that the studied aniline derivatives are not hydrophilic compounds and this means these compounds hardly dissolve in water or an aqueous solvent.