RESUMEN
Three-dimensional quantitative structure-activity relationship studies were carried out on a series of 28 organosulphur compounds as 15-lipoxygenase inhibitors using comparative molecular field analysis and comparative molecular similarity indices analysis. Quantitative information on structure-activity relationships is provided for further rational development and direction of selective synthesis. All models were carried out over a training set including 22 compounds. The best comparative molecular field analysis model only included steric field and had a good Q² = 0.789. Comparative molecular similarity indices analysis overcame the comparative molecular field analysis results: the best comparative molecular similarity indices analysis model also only included steric field and had a Q² = 0.894. In addition, this model predicted adequately the compounds contained in the test set. Furthermore, plots of steric comparative molecular similarity indices analysis field allowed conclusions to be drawn for the choice of suitable inhibitors. In this sense, our model should prove useful in future 15-lipoxygenase inhibitor design studies.
Asunto(s)
Araquidonato 15-Lipooxigenasa , Simulación por Computador , Glycine max/química , Inhibidores de la Lipooxigenasa/síntesis química , Inhibidores de la Lipooxigenasa/farmacología , Compuestos Orgánicos , Azufre , Araquidonato 15-Lipooxigenasa/química , Concentración 50 Inhibidora , Ligandos , Inhibidores de la Lipooxigenasa/química , Modelos Químicos , Modelos Moleculares , Compuestos Orgánicos/química , Compuestos Orgánicos/farmacología , Relación Estructura-Actividad Cuantitativa , Azufre/química , Azufre/farmacologíaRESUMEN
We have performed the docking of sulfonyl hydrazides complexed with cytosolic branched-chain amino acid aminotransferase (BCATc) to study the orientations and preferred active conformations of these inhibitors. The study was conducted on a selected set of 20 compounds with variation in structure and activity. In addition, the predicted inhibitor concentration (IC(50)) of the sulfonyl hydrazides as BCAT inhibitors were obtained by a quantitative structure-activity relationship (QSAR) method using three-dimensional (3D) vectors. We found that three-dimensional molecule representation of structures based on electron diffraction (3D-MoRSE) scheme contains the most relevant information related to the studied activity. The statistical parameters [cross-validate correlation coefficient (Q(2) = 0.796) and fitted correlation coefficient (R(2) = 0.899)] validated the quality of the 3D-MoRSE predictive model for 16 compounds. Additionally, this model adequately predicted four compounds that were not included in the training set.
Asunto(s)
Citosol/enzimología , Hidrazinas/química , Hidrazinas/farmacología , Modelos Moleculares , Relación Estructura-Actividad Cuantitativa , Transaminasas/antagonistas & inhibidores , Dominio Catalítico , Diseño de Fármacos , Inhibidores Enzimáticos/química , Inhibidores Enzimáticos/farmacología , Humanos , Modelos Lineales , Transaminasas/químicaRESUMEN
A target-ligand QSAR approach using autocorrelation formalism was developed for modeling the inhibitory potency (pIC(50)) toward matrix metalloproteinases (MMP-1, MMP-2, MMP-3, MMP-9, and MMP-13) of N-hydroxy-2-[(phenylsulfonyl)amino]acetamide derivatives. Target and ligand structural information was encoded in the Topological Autocorrelation Interaction matrix calculated from 2D topological representation of inhibitors and protein sequences. The relevant Topological Autocorrelation Interaction descriptors were selected by genetic algorithm-based multilinear regression analysis and Bayesian-regularized genetic neural network approaches. A model ensemble strategy was employed for achieving robust and reliable linear and non-linear predictors having nine topological autocorrelation interaction descriptors with square correlation coefficients of ensemble test-set fitting (R(2)(test)) about 0.80 and 0.87, respectively. Electrostatic and hydrophobicity/hydrophilicity properties were the most relevant on the optimum models. In addition, the distribution of the inhibition complexes on a self-organized map depicted target dependence rather than an inhibitor similarity pattern.
Asunto(s)
Acetamidas/química , Inhibidores de la Metaloproteinasa de la Matriz , Modelos Moleculares , Algoritmos , Inhibidores Enzimáticos/química , Interacciones Hidrofóbicas e Hidrofílicas , Metaloproteinasas de la Matriz/química , Redes Neurales de la Computación , Relación Estructura-Actividad Cuantitativa , Electricidad EstáticaRESUMEN
2D autocorrelation, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were undertaken for a series of pyrido[2,3-d]pyrimidin-7-ones to correlate cyclin-dependent kinase (CDK) cyclin D/CDK4 inhibition with 2D and 3D structural properties of 60 known compounds. QSAR models with considerable internal as well as external predictive ability were obtained. The relevant 2D autocorrelation descriptors for modeling CDK4/D inhibitory activity were selected by linear and nonlinear genetic algorithms (GAs) using multiple linear regression (MLR) and Bayesian-regularized genetic neural network (BRGNN) approaches, respectively. Both models showed good predictive statistics; but BRGNN model enables better external predictions. A weight-based input ranking scheme and Kohonen self-organized maps (SOMs) were carried out to interpret the final net weights. The 2D autocorrelation space brings different descriptors for CDK4/D inhibition, and suggests the atomic properties relevant for the inhibitors to interact with CDK4/D active site. CoMFA and CoMSIA analyses were developed with a focus on interpretative ability using coefficient contour maps. CoMSIA produced significantly better results. The results indicate a strong correlation between the inhibitory activity of the modeled compounds and the electrostatic and hydrophobic fields around them.
Asunto(s)
Quinasa 4 Dependiente de la Ciclina/antagonistas & inhibidores , Quinasa 4 Dependiente de la Ciclina/química , Ciclinas/antagonistas & inhibidores , Ciclinas/química , Inhibidores de Proteínas Quinasas/química , Pirimidinonas/química , Algoritmos , Teorema de Bayes , Simulación por Computador , Ciclina D , Interacciones Hidrofóbicas e Hidrofílicas , Modelos Lineales , Modelos Químicos , Modelos Moleculares , Redes Neurales de la Computación , Valor Predictivo de las Pruebas , Inhibidores de Proteínas Quinasas/farmacología , Pirimidinonas/farmacología , Relación Estructura-Actividad Cuantitativa , Electricidad EstáticaRESUMEN
The structural requirements of pyrrolo[2,3-d]pyrimidine nucleoside (PPN) analogues as adenosine kinase (AK) inhibitors were in silico studied by using CoMSIA method. All models were trained with 32 compounds, after which they were evaluated for predictive ability with additional 5 compounds. Quantitative information on structure-activity trends is provided for further rational development and direction of selective synthesis. The best CoMSIA model included hydrophobic, H-bond donor and H-bond acceptor fields and had a good predictive quality according to internal validation criteria. In addition, this model predicted adequately the compounds contained in the test set. The analysis of the model gives a comprehensive qualitative and quantitative description of the molecular features at C4 and C5 positions of the pyrrolo[2,3-d]pyrimidine scaffold and C5-position of the beta-d-ribofuranose of PPN analogues, relevant for a high AK inhibitory activity.
Asunto(s)
Adenosina Quinasa/antagonistas & inhibidores , Simulación por Computador , Modelos Químicos , Nucleósidos/farmacología , Pirimidinas/farmacología , Pirroles/farmacología , Adenosina Quinasa/química , Bases de Datos Factuales , Inhibidores Enzimáticos , Modelos Moleculares , Estructura Molecular , Nucleósidos/química , Pirimidinas/química , Pirroles/química , Relación Estructura-Actividad Cuantitativa , Reproducibilidad de los Resultados , EstereoisomerismoRESUMEN
Voltage-gated K(+) ion channels (VKCs) are membrane proteins that regulate the passage of potassium ions through membranes. This work reports a classification scheme of VKCs according to the signs of three electrophysiological variables: activation threshold voltage (V(t)), half-activation voltage (V(a50)) and half-inactivation voltage (V(h50)). A novel 3D pseudo-folding graph representation of protein sequences encoded the VKC sequences. Amino acid pseudo-folding 3D distances count (AAp3DC) descriptors, calculated from the Euclidean distances matrices (EDMs) were tested for building the classifiers. Genetic algorithm (GA)-optimized support vector machines (SVMs) with a radial basis function (RBF) kernel well discriminated between VKCs having negative and positive/zero V(t), V(a50) and V(h50) values with overall accuracies about 80, 90 and 86%, respectively, in crossvalidation test. We found contributions of the "pseudo-core" and "pseudo-surface" of the 3D pseudo-folded proteins to the discrimination between VKCs according to the three electrophysiological variables.
Asunto(s)
Canales de Potasio con Entrada de Voltaje/química , Canales de Potasio con Entrada de Voltaje/clasificación , Pliegue de Proteína , Algoritmos , Secuencia de Aminoácidos , Inteligencia Artificial , Datos de Secuencia Molecular , Canales de Potasio con Entrada de Voltaje/genética , Reproducibilidad de los ResultadosRESUMEN
This work reports a novel 3D pseudo-folding graph representation of protein sequences for modeling purposes. Amino acids euclidean distances matrices (EDMs) encode primary structural information. Amino Acid Pseudo-Folding 3D Distances Count (AAp3DC) descriptors, calculated from the EDMs of a large data set of 1363 single protein mutants of 64 proteins, were tested for building a classifier for the signs of the change of thermal unfolding Gibbs free energy change (DeltaDeltaG) upon single mutations. An optimum support vector machine (SVM) with a radial basis function (RBF) kernel well recognized stable and unstable mutants with accuracies over 70% in crossvalidation test. To the best of our knowledge, this result for stable mutant recognition is the highest ever reported for a sequence-based predictor with more than 1000 mutants. Furthermore, the model adequately classified mutations associated to diseases of human prion protein and human transthyretin.
Asunto(s)
Mutación Puntual , Pliegue de Proteína , Proteínas/química , Secuencia de Aminoácidos , Animales , Humanos , Datos de Secuencia Molecular , Conformación Proteica , Proteínas/genéticaRESUMEN
2D Autocorrelation, comparative molecular field analysis (CoMFA), and comparative molecular similarity indices analysis (CoMSIA) were undertaken for a series of substituted pyrido[2,3-d]pyrimidine derivatives to correlate platelet-derived growth factor receptor (PDGFR), fibroblast growth factor receptor (FGFR), and c-Src tyrosine kinases' inhibition with 2D and 3D structural properties of 22 known compounds. QSAR models with considerable internal as well as external predictive ability were obtained. The relevant 2D autocorrelation descriptors for modeling each protein tyrosine kinase (PTK) inhibitory activity were selected by genetic algorithm (GA) and multiple linear regression (MLR) approach. The 2D autocorrelation space brings different descriptors for each PTK inhibition and suggests the atomic properties relevant for the inhibitors to interact with each PTK active site. CoMFA and CoMSIA were developed with a focus on interpretative ability using coefficient contour maps. CoMSIA produced significantly better results for all correlations. The results indicate a strong correlation between the inhibitory activity of the modeled compounds and the hydrophobic and H-bond donor fields around them.
Asunto(s)
Modelos Moleculares , Proteínas Tirosina Quinasas/antagonistas & inhibidores , Pirimidinas/síntesis química , Pirimidinas/farmacocinética , Algoritmos , Diseño de Fármacos , Enlace de Hidrógeno , Conformación Molecular , Estructura Molecular , Pirimidinas/química , Relación Estructura-Actividad Cuantitativa , Receptores de Factores de Crecimiento de Fibroblastos/antagonistas & inhibidores , Receptores del Factor de Crecimiento Derivado de Plaquetas/antagonistas & inhibidores , Familia-src Quinasas/antagonistas & inhibidoresRESUMEN
Three-dimensional quantitative structure-activity relationship (3D-QSAR) studies were carried out on a series of 38 rubiscolins as delta opioid peptides using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Quantitative information on structure-activity relationships is provided for further rational development and direction of selective synthesis. All models were carried out over a training set including 30 peptides. The best CoMFA model included electrostatic and steric fields and had a moderate Q (2) = 0.503. CoMSIA analysis surpassed the CoMFA results: the best CoMSIA model included only the hydrophobic field and had a Q (2) = 0.661. In addition, this model predicted adequately the peptides contained in the test set. Our model identified that the potency of delta opioid activity of rubiscolin analogues essentially exhibited a significant relationship with local hydrophobic and hydrophilic characteristics of amino acids at positions 3, 4, 5, and 6.
Asunto(s)
Relación Estructura-Actividad Cuantitativa , Receptores Opioides delta/agonistas , Ribulosa-Bifosfato Carboxilasa/química , Ribulosa-Bifosfato Carboxilasa/farmacología , Fenómenos Químicos , Química Física , Modelos MolecularesRESUMEN
The main molecular features which determine the selectivity of a set of 80 N-hydroxy-alpha-phenylsulfonylacetamide derivatives (HPSAs) in the inhibition of three matrix metalloproteinases (MMP-1, MMP-9, and MMP-13) have been identified by using linear and nonlinear predictive models. The molecular information has been encoded in 2D autocorrelation descriptors, obtained from different weighting schemes. The linear models were built by multiple linear regression (MLR) combined with genetic algorithm (GA), and a robust QSAR mapping paradigm. The Bayesian-regularized genetic neural network (BRGNN) was employed for nonlinear modeling. In such approaches each model could have its own set of input variables. All models were predictive according to internal and external validation experiments; but the best results correspond to nonlinear ones. The 2D autocorrelation space brings different descriptors for each MMP inhibition, and suggests the atomic properties relevant for the inhibitors to interact with each MMP active site. On the basis of the current results, the reported models have the potential to discover new potent and selective inhibitors and bring useful molecular information about the ligand specificity for MMP S(1)(') and S(2)(') subsites.
Asunto(s)
Acetamidas/farmacología , Modelos Lineales , Inhibidores de la Metaloproteinasa de la Matriz , Modelos Moleculares , Inhibidores de Proteasas/farmacología , Relación Estructura-Actividad Cuantitativa , Acetamidas/síntesis química , Acetamidas/química , Algoritmos , Teorema de Bayes , Simulación por Computador , Ligandos , Modelos Biológicos , Estructura Molecular , Redes Neurales de la Computación , Inhibidores de Proteasas/síntesis química , Inhibidores de Proteasas/química , Ésteres del Ácido Sulfúrico/químicaRESUMEN
Development of novel computational approaches for modeling protein properties is a main goal in applied Proteomics. In this work, we reported the extension of the radial distribution function (RDF) scores formalism to proteins for encoding 3D structural information with modeling purposes. Protein-RDF (P-RDF) scores measure spherical distributions on protein 3D structure of 48 amino acids/residues properties selected from the AAindex data base. P-RDF scores were tested for building predictive models of the change of thermal unfolding Gibbs free energy change (DeltaDeltaG) of chymotrypsin inhibitor 2 upon mutations. In this sense, an ensemble of Bayesian-Regularized Genetic Neural Networks (BRGNNs) yielded an optimum nonlinear model for the conformational stability. The ensemble predictor described about 84% and 70% variance of the data in training and test sets, respectively.
Asunto(s)
Teorema de Bayes , Redes Neurales de la Computación , Péptidos/química , Proteínas de Plantas/química , Proteínas/química , Algoritmos , Biología Computacional/métodos , Mutación , Péptidos/genética , Proteínas de Plantas/genética , Conformación ProteicaRESUMEN
Development of novel computational approaches for modeling protein properties from their primary structure is the main goal in applied proteomics. In this work, we reported the extension of the autocorrelation vector formalism to amino acid sequences for encoding protein structural information with modeling purposes. Amino acid sequence autocorrelation (AASA) vectors were calculated by measuring the autocorrelations at sequence lags ranging from 1 to 15 on the protein primary structure of 48 amino acid/residue properties selected from the AAindex data base. A total of 720 AASA descriptors were tested for building predictive models of the change of thermal unfolding Gibbs free energy change (delta deltaG) of gene V protein upon mutation. In this sense, ensembles of Bayesian-regularized genetic neural networks (BRGNNs) were used for obtaining an optimum nonlinear model for the conformational stability. The ensemble predictor described about 88% and 66% variance of the data in training and test sets respectively. Furthermore, the optimum AASA vector subset not only helped to successfully model unfolding stability but also well distributed wild-type and gene V protein mutants on a stability self-organized map (SOM), when used for unsupervised training of competitive neurons.
Asunto(s)
Vectores Genéticos/genética , Modelos Biológicos , Conformación Proteica , Proteínas/química , Proteínas/genética , Secuencia de Aminoácidos , Fenómenos Químicos , Química Física , Biología Computacional , Simulación por Computador , Mutación/genética , Redes Neurales de la Computación , Pliegue de Proteína , Proteínas/metabolismoRESUMEN
Growth hormone secretagogue agonist activities for a data set of 45 tetrahydroisoquinoline 1-carboxamides were modeled using several kinds of molecular descriptors from dragon software. A linear model with six variables selected from a large pool of two-dimensional descriptors described 80% of cross-validation data variance. Similar results were found for a model obtained from a pool of three-dimensional descriptors. Size and hydrophilicity-related atomic properties such as mass, polarizability, and van der Waals volume were determined to be the most relevant for the differential growth hormone secretagogue agonist activities of the compounds studied. In addition, Artificial Neural Networks were trained using optimum variables from the linear models; however, they were found to overfit the data and resulted in similar or lower predictive power.
Asunto(s)
Hormona del Crecimiento/farmacología , Redes Neurales de la Computación , Relación Estructura-Actividad Cuantitativa , Receptores Acoplados a Proteínas G/agonistas , Tetrahidroisoquinolinas/farmacología , Biología Computacional , Hormona del Crecimiento/química , Modelos Lineales , Unión Proteica , Receptores Acoplados a Proteínas G/metabolismo , Receptores de Ghrelina , Programas Informáticos , Tetrahidroisoquinolinas/químicaRESUMEN
We perform linear regression analyses on 1202 numerical descriptors that encode the various aspects of the topological, geometrical and electronic molecular structure with the aim of achieving the best QSAR relationship between the antifungal potencies against the Candida albicans strain and the structure of 96 heterocyclic ring derivatives. As a realistic application we employ the model found to predict the biological activity for 60 non-yet measured compounds.
Asunto(s)
Antifúngicos/síntesis química , Antifúngicos/farmacología , Compuestos Heterocíclicos/síntesis química , Compuestos Heterocíclicos/farmacología , Algoritmos , Candida albicans/efectos de los fármacos , Biología Computacional , Interpretación Estadística de Datos , Pruebas de Sensibilidad Microbiana , Modelos Moleculares , Modelos Estadísticos , Relación Estructura-Actividad Cuantitativa , Análisis de RegresiónRESUMEN
Multiple linear regression (MLR) combined with genetic algorithm (GA) and Bayesian-regularized Genetic Neural Networks (BRGNNs) were used to model the binding affinity (pK(I)) of 38 11,12-cyclic carbamate derivatives of 6-O-methylerythromycin A for the Human Luteinizing Hormone-Releasing Hormone (LHRH) receptor using quantum chemical descriptors. A multiparametric MLR equation with good statistical quality was obtained that describes the features relevant for antagonistic activity when the substituent at the position 3 of the erythronolide core was varied. In addition, four-descriptor linear and nonlinear models were established for the whole dataset. Such models showed high statistical quality. However, the BRGNN model was better than the linear model according to the external validation process. In general, our linear and nonlinear models reveal that the binding affinity of the compounds studied for the LHRH receptor is modulated by electron-related terms.
Asunto(s)
Eritromicina/análogos & derivados , Hormona Liberadora de Gonadotropina/antagonistas & inhibidores , Modelos Químicos , Relación Estructura-Actividad Cuantitativa , Animales , Teorema de Bayes , Células CHO , Cricetinae , Cricetulus , Humanos , Modelos Lineales , Redes Neurales de la Computación , Péptidos , Teoría Cuántica , Programas InformáticosRESUMEN
Functional variations on the human ghrelin receptor upon mutations have been associated with a syndrome of short stature and obesity, of which the obesity appears to develop around puberty. In this work, we reported a proteometrics analysis of the constitutive and ghrelin-induced activities of wild-type and mutant ghrelin receptors using amino acid sequence autocorrelation (AASA) approach for protein structural information encoding. AASA vectors were calculated by measuring the autocorrelations at sequence lags ranging from 1 to 15 on the protein primary structure of 48 amino acid/residue properties selected from the AAindex database. Genetic algorithm-based multilinear regression analysis (GA-MRA) and genetic algorithm-based least square support vector machines (GA-LSSVM) were used for building linear and non-linear models of the receptor activity. A genetic optimized radial basis function (RBF) kernel yielded the optimum GA-LSSVM models describing 88% and 95% of the cross-validation variance for the constitutive and ghrelin-induced activities, respectively. AASA vectors in the optimum models mainly appeared weighted by hydrophobicity-related properties. However, differently to the constitutive activity, the ghrelin-induced activity was also highly dependent of the steric features of the receptor.
Asunto(s)
Proteómica/métodos , Receptores Acoplados a Proteínas G/química , Receptores Acoplados a Proteínas G/genética , Algoritmos , Secuencia de Aminoácidos , Inteligencia Artificial , Bases de Datos de Proteínas , Humanos , Técnicas In Vitro , Análisis de los Mínimos Cuadrados , Modelos Lineales , Modelos Moleculares , Mutación , Dinámicas no Lineales , Proteómica/estadística & datos numéricos , Relación Estructura-Actividad Cuantitativa , Receptores Acoplados a Proteínas G/metabolismo , Receptores de GhrelinaRESUMEN
Acetylcholinesterase inhibition was modeled for a set of huprines using ensembles of Bayesian-regularized Genetic Neural Networks. In the Bayesian-regularized Genetic Neural Network approach the Bayesian regularization avoids overfitted regressions and the genetic algorithm allows exploring a wide pool of three-dimensional descriptors. The predictive capacity of our selected model was evaluated by averaging multiple validation sets generated as members of neural network ensembles. When 60 members are assembled, the neural network ensemble provides a reliable measure of training and test set R(2)-values of 0.945 and 0.850 respectively. In other respects, the ability of the nonlinear selected genetic algorithm space for differentiate the data were evidenced when total data set was well distributed in a Kohonen self-organizing map. The analysis of the self-organizing map zones allows establishing the main structural features differentiated by our vectorial space.
Asunto(s)
Acetilcolinesterasa/química , Teorema de Bayes , Inhibidores de la Colinesterasa/química , Redes Neurales de la Computación , Acetilcolinesterasa/genética , Acetilcolinesterasa/metabolismo , Algoritmos , Alcaloides/química , Alcaloides/farmacología , Animales , Bovinos , Inhibidores de la Colinesterasa/farmacología , Simulación por Computador , Diseño de Fármacos , Modelos Biológicos , Estructura Molecular , Relación Estructura-Actividad Cuantitativa , Sesquiterpenos/química , Sesquiterpenos/farmacología , Tacrina/química , Tacrina/farmacologíaRESUMEN
Acetylcholinesterase inhibition was modeled for a set of huprines using ensembles of Bayesian-regularized Genetic Neural Networks. In the Bayesian-regularized Genetic Neural Network approach the Bayesian regularization avoids overfitted regressions and the genetic algorithm allows exploring a wide pool of three-dimensional descriptors. The predictive capacity of our selected model was evaluated by averaging multiple validation sets generated as members of neural network ensembles. When 60 members are assembled, the neural network ensemble provides a reliable measure of training and test set R2-values of 0.945 and 0.850 respectively. In other respects, the ability of the nonlinear selected genetic algorithm space for differentiate the data were evidenced when total data set was well distributed in a Kohonen self-organizing map. The analysis of the self-organizing map zones allows establishing the main structural features differentiated by our vectorial space(AU)
Asunto(s)
Animales , Inhibidores de la Colinesterasa/química , Inhibidores de la Colinesterasa/farmacología , Teorema de BayesRESUMEN
The inhibitory activity towards p34(cdc2)/cyclin b kinase (CBK) enzyme of 30 cytokinin-derived compounds has been successfully modelled using 2D spatial autocorrelation vectors. Predictive linear and non-linear models were obtained by forward stepwise multi-linear regression analysis (MRA) and artificial neural network (ANN) approaches respectively. A variable selection routine that selected relevant non-linear information from the data set was employed prior to networks training. The best ANN with three input variables was able to explain about 87% data variance in comparison with 80% by the linear equation using the same number of descriptors. Similarly, the neural network had higher predictive power. The MRA model showed a linear dependence between the inhibitory activities and the spatial distributions of masses, electronegativities and van der Waals volumes on the inhibitors molecules. Meanwhile, ANN model evidenced the occurrence of non-linear relationships between the inhibitory activity and the mass distribution at different topological distance on the cytokinin-derived compounds. Furthermore, inhibitors were well distributed regarding its activity levels in a Kohonen self-organizing map (SOM) built using the input variables of the best neural network.
Asunto(s)
Quinasas Ciclina-Dependientes/antagonistas & inhibidores , Citocininas/farmacología , Modelos Biológicos , Animales , Proteína Quinasa CDC2/antagonistas & inhibidores , Citocininas/química , Femenino , Técnicas In Vitro , Matemática , Redes Neurales de la Computación , Inhibidores de Proteínas Quinasas/química , Inhibidores de Proteínas Quinasas/farmacología , Análisis de Regresión , Estrellas de Mar/enzimología , Relación Estructura-ActividadRESUMEN
By means of QSAR algorithms we model the potency pIC(90) [mM] of 154 non-nucleoside reverse transcriptase inhibitors (NNRTI) of the wild-type HIV-1 virus, considered as the second generation analogues of Efavirenz. In addition, 56 inhibitors of the K-103N viral mutant form are also investigated. A pool of 1494 theoretical molecular descriptors provided mainly by the Dragon 5 software is explored by several methods of variable selection: forward stepwise regression, the replacement method, and the genetic algorithm approach. The optimal models found include up to seven parameters: R = 0.7991, R(l-20%-o) = 0.7233 for the case of wild-type, and R = 0.9261, R(l-5%-o) = 0.8802 for the K-103N mutation.