Quantitative Structure-Property Relationships for Predicting the Retention Indices of Fragrances on Stationary Phases of Different Polarity

El objetivo de este trabajo fue el desarrollo de relaciones cuantitativas estructura–propiedad predictivas para el modelado de índices de retención (I) de fragancias, medidas en tres fases estacionarias de diferente polaridad: DB–225MS, HP5–MS y HP–1. Se ha prestado particular atención al curado de los datos experimentales. Posteriormente, se usó el método de subconjuntos balanceados (BSM) para dividir cada base de datos en grupos de calibración, validación y predicción. Los modelos se construyeron a partir de 1819 descriptores moleculares independientes de la conformación, los cuales fueron analizados mediante el método de reemplazo (RM) para la selección de los mismos, con la finalidad de obtener los mejores modelos. Para la fase estacionaria DB–225MS se obtuvo un modelo basado en cuatro descriptores, mientras que para las columnas HP5–MS y HP–1 se propusieron modelos con tres descriptores. Los modelos fueron validados mediante validación cruzada de dejar–uno–fuera y dejar–varios–fuera, así como otros criterios de validación. Adicionalmente, con la finalidad de cumplir los principios propuestos por la Organization for Economic Co–operation and Development (OECD), la capacidad predictiva de los modelos se evaluó mediante la predicción de los índices de retención del grupo externo de predicción, el dominio de aplicabilidad fue apropiadamente definido y se realizó una interpretación de cada descriptor molecular involucrado.The purpose of this work was to develop predictive quantitative structure–property relationships for modeling the retention indices (I) of fragrances measured in three stationary phases of different polarities: DB–225MS, HP5–MS and HP–1. Attention was paid to the curation of the experimental data. Subsequently, the Balanced Subsets method (BSM) was used to split each dataset into training, validation and test sets. Models were established by using 1819 conformation–independent molecular descriptors which were analyzed by the replacement method (RM) variable subset selection in order to obtain the optimal models. A four–descriptor model was obtained for the DB–225MS stationary phase while a three–parametric model was proposed for both the HP5–MS and HP–1 columns. Models were validated by means of the leave–one–out and leave–many–out cross–validation procedures, as well as other validation criteria. Moreover, in order to accomplish the principles proposed by the Organization for Economic Co–operation and Development (OECD), the model’s predictive ability was measured by predicting retention indices of the external test set. The applicability domain was properly defined and the interpretation of each of the molecular descriptors used in this study was provided.Fil: Rojas Villa, Cristian Xavier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; ArgentinaFil: Duchowicz, Pablo Román. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; ArgentinaFil: Tripaldi, P.. Universidad de Azuay; EcuadorFil: Pis Diez, Reinaldo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Química Inorgánica "Dr. Pedro J. Aymonino". Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Centro de Química Inorgánica "Dr. Pedro J. Aymonino"; Argentin

Current Mathematical Methods Used in QSAR/QSPR Studies

This paper gives an overview of the mathematical methods currently used in quantitative structure-activity/property relationship (QASR/QSPR) studies. Recently, the mathematical methods applied to the regression of QASR/QSPR models are developing very fast, and new methods, such as Gene Expression Programming (GEP), Project Pursuit Regression (PPR) and Local Lazy Regression (LLR) have appeared on the QASR/QSPR stage. At the same time, the earlier methods, including Multiple Linear Regression (MLR), Partial Least Squares (PLS), Neural Networks (NN), Support Vector Machine (SVM) and so on, are being upgraded to improve their performance in QASR/QSPR studies. These new and upgraded methods and algorithms are described in detail, and their advantages and disadvantages are evaluated and discussed, to show their application potential in QASR/QSPR studies in the future

Foodinformatic prediction of the retention time of pesticide residues detected in fruits and vegetables using UHPLC/ESI Q-Orbitrap

The present work describes the development of an in silico model to predict the retention time (tR) of a large Compound DataBase (CDB) of pesticides detected in fruits and vegetables. The model utilizes ultrahigh-performance liquid chromatography electrospray ionization quadrupole-Orbitrap (UHPLC/ESI Q-Orbitrap) mass spectrometry (MS) data. The available CDB was properly curated, and the pesticides were represented by conformation-independent molecular descriptors. In an attempt to improve the model predictions, the best four MLR models obtained were subjected to a consensus analysis. The optimal model was evaluated by means of the coefficient of determination and the residual standard deviation in calibration, validation, and prediction, along other internal and external validation criteria to accomplish the guidelines defined by the Organization for Economic Co-operation and Development. Finally, the in silico model was applied to predict the tR of an external set of 57 pesticides.Fil: Rojas, Cristian. Universidad del Azuay; EcuadorFil: Aranda, José Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; ArgentinaFil: Pacheco Jaramillo, Elisa. Universidad del Azuay; EcuadorFil: Losilla Bermejo, Irene. Universidad de Extremadura; España. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Tripaldi, Piercosimo. Universidad del Azuay; EcuadorFil: Duchowicz, Pablo Román. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; ArgentinaFil: Castro, Eduardo Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; Argentin

NOVEL ALGORITHMS AND TOOLS FOR LIGAND-BASED DRUG DESIGN

Computer-aided drug design (CADD) has become an indispensible component in modern drug discovery projects. The prediction of physicochemical properties and pharmacological properties of candidate compounds effectively increases the probability for drug candidates to pass latter phases of clinic trials. Ligand-based virtual screening exhibits advantages over structure-based drug design, in terms of its wide applicability and high computational efficiency. The established chemical repositories and reported bioassays form a gigantic knowledgebase to derive quantitative structure-activity relationship (QSAR) and structure-property relationship (QSPR). In addition, the rapid advance of machine learning techniques suggests new solutions for data-mining huge compound databases. In this thesis, a novel ligand classification algorithm, Ligand Classifier of Adaptively Boosting Ensemble Decision Stumps (LiCABEDS), was reported for the prediction of diverse categorical pharmacological properties. LiCABEDS was successfully applied to model 5-HT1A ligand functionality, ligand selectivity of cannabinoid receptor subtypes, and blood-brain-barrier (BBB) passage. LiCABEDS was implemented and integrated with graphical user interface, data import/export, automated model training/ prediction, and project management. Besides, a non-linear ligand classifier was proposed, using a novel Topomer kernel function in support vector machine. With the emphasis on green high-performance computing, graphics processing units are alternative platforms for computationally expensive tasks. A novel GPU algorithm was designed and implemented in order to accelerate the calculation of chemical similarities with dense-format molecular fingerprints. Finally, a compound acquisition algorithm was reported to construct structurally diverse screening library in order to enhance hit rates in high-throughput screening

Dibenzoylhydrazines as Insect Growth Modulators: Topology-Based QSAR Modelling

Dibenzoylhydrazines Xa-(C6H5)a-CO-N-(t-Bu)-NH-CO-(C6H5)b-Yb are efficient insect growth regulators with high activity and selectivity toward lepidopteran and coleopteran pests. For 123 congeneric molecules, a quantitative structure activity relationship model was built in the framework of the QSARINS package using 2D, Topology-based, PaDEL descriptors. Variable selection by GA-MLR allows building an efficient multilinear regression linking pEC50 values to nine structural variables. Robustness and quality of the model were carefully examined at various levels: data-fitting (recall), leave-one (or some) - out, internal and external validation (including random splitting), points not in depth investigated in previous works. Various Machine Learning approaches (Partial Least Squares Regression, Projection Pursuit Regression, Linear Support Vector Machine or Three Layer Perceptron Artificial Neural Network) confirm the validity of the analysis, giving highly consistent results of comparable quality, with only a slight advantage for the three-layer perceptron

Application of Multivariate Adaptive Regression Splines (MARSplines) for Predicting Hansen Solubility Parameters Based on 1D and 2D Molecular Descriptors Computed from SMILES String

A new method of Hansen solubility parameters (HSPs) prediction was developed by combining the multivariate adaptive regression splines (MARSplines) methodology with a simple multivariable regression involving 1D and 2D PaDEL molecular descriptors. In order to adopt the MARSplines approach to QSPR/QSAR problems, several optimization procedures were proposed and tested. The effectiveness of the obtained models was checked via standard QSPR/QSAR internal validation procedures provided by the QSARINS software and by predicting the solubility classification of polymers and drug-like solid solutes in collections of solvents. By utilizing information derived only from SMILES strings, the obtained models allow for computing all of the three Hansen solubility parameters including dispersion, polarization, and hydrogen bonding. Although several descriptors are required for proper parameters estimation, the proposed procedure is simple and straightforward and does not require a molecular geometry optimization. The obtained HSP values are highly correlated with experimental data, and their application for solving solubility problems leads to essentially the same quality as for the original parameters. Based on provided models, it is possible to characterize any solvent and liquid solute for which HSP data are unavailable

Quantitative Structure-Property Relationships for Predicting the Retention Indices of Fragrances on Stationary Phases of Different Polarity

El objetivo de este trabajo fue el desarrollo de relaciones cuantitativas estructura–propiedad predictivas para el modelado de índices de retención (I) de fragancias, medidas en tres fases estacionarias de diferente polaridad: DB–225MS, HP5–MS y HP–1. Se ha prestado particular atención al curado de los datos experimentales. Posteriormente, se usó el método de subconjuntos balanceados (BSM) para dividir cada base de datos en grupos de calibración, validación y predicción. Los modelos se construyeron a partir de 1819 descriptores moleculares independientes de la conformación, los cuales fueron analizados mediante el método de reemplazo (RM) para la selección de los mismos, con la finalidad de obtener los mejores modelos. Para la fase estacionaria DB–225MS se obtuvo un modelo basado en cuatro descriptores, mientras que para las columnas HP5–MS y HP–1 se propusieron modelos con tres descriptores. Los modelos fueron validados mediante validación cruzada de dejar–uno–fuera y dejar–varios–fuera, así como otros criterios de validación. Adicionalmente, con la finalidad de cumplir los principios propuestos por la Organization for Economic Cooperation and Development (OECD), la capacidad predictiva de los modelos se evaluó mediante la predicción de los índices de retención del grupo externo de predicción, el dominio de aplicabilidad fue apropiadamente definido y se realizó una interpretación de cada descriptor molecular involucrado.The purpose of this work was to develop predictive quantitative structure–property relationships for modeling the retention indices (I) of fragrances measured in three stationary phases of different polarities: DB–225MS, HP5–MS and HP–1. Attention was paid to the curation of the experimental data. Subsequently, the Balanced Subsets method (BSM) was used to split each dataset into training, validation and test sets. Models were established by using 1819 conformation–independent molecular descriptors which were analyzed by the replacement method (RM) variable subset selection in order to obtain the optimal models. A four–descriptor model was obtained for the DB–225MS stationary phase while a three–parametric model was proposed for both the HP5–MS and HP–1 columns. Models were validated by means of the leave–one–out and leave–many–out cross–validation procedures, as well as other validation criteria. Moreover, in order to accomplish the principles proposed by the Organization for Economic Co–operation and Development (OECD), the model’s predictive ability was measured by predicting retention indices of the external test set. The applicability domain was properly defined and the interpretation of each of the molecular descriptors used in this study was provided.Instituto de Investigaciones Fisicoquímicas Teóricas y AplicadasCentro de Química Inorgánic

Quantitative Structure-Property Relationships for Predicting the Retention Indices of Fragrances on Stationary Phases of Different Polarity

El objetivo de este trabajo fue el desarrollo de relaciones cuantitativas estructura–propiedad predictivas para el modelado de índices de retención (I) de fragancias, medidas en tres fases estacionarias de diferente polaridad: DB–225MS, HP5–MS y HP–1. Se ha prestado particular atención al curado de los datos experimentales. Posteriormente, se usó el método de subconjuntos balanceados (BSM) para dividir cada base de datos en grupos de calibración, validación y predicción. Los modelos se construyeron a partir de 1819 descriptores moleculares independientes de la conformación, los cuales fueron analizados mediante el método de reemplazo (RM) para la selección de los mismos, con la finalidad de obtener los mejores modelos. Para la fase estacionaria DB–225MS se obtuvo un modelo basado en cuatro descriptores, mientras que para las columnas HP5–MS y HP–1 se propusieron modelos con tres descriptores. Los modelos fueron validados mediante validación cruzada de dejar–uno–fuera y dejar–varios–fuera, así como otros criterios de validación. Adicionalmente, con la finalidad de cumplir los principios propuestos por la Organization for Economic Cooperation and Development (OECD), la capacidad predictiva de los modelos se evaluó mediante la predicción de los índices de retención del grupo externo de predicción, el dominio de aplicabilidad fue apropiadamente definido y se realizó una interpretación de cada descriptor molecular involucrado.The purpose of this work was to develop predictive quantitative structure–property relationships for modeling the retention indices (I) of fragrances measured in three stationary phases of different polarities: DB–225MS, HP5–MS and HP–1. Attention was paid to the curation of the experimental data. Subsequently, the Balanced Subsets method (BSM) was used to split each dataset into training, validation and test sets. Models were established by using 1819 conformation–independent molecular descriptors which were analyzed by the replacement method (RM) variable subset selection in order to obtain the optimal models. A four–descriptor model was obtained for the DB–225MS stationary phase while a three–parametric model was proposed for both the HP5–MS and HP–1 columns. Models were validated by means of the leave–one–out and leave–many–out cross–validation procedures, as well as other validation criteria. Moreover, in order to accomplish the principles proposed by the Organization for Economic Co–operation and Development (OECD), the model’s predictive ability was measured by predicting retention indices of the external test set. The applicability domain was properly defined and the interpretation of each of the molecular descriptors used in this study was provided.Instituto de Investigaciones Fisicoquímicas Teóricas y AplicadasCentro de Química Inorgánic