Daniel E. Bacelo

QSAR on antiproliferative naphthoquinones based on a conformation-independent approach

The antiproliferative activities of a series of 36 naphthoquinone derivatives were subjected to a Quantitative Structure-Activity Relationships (QSAR) study. For this purpose a panel of four human cancer cell lines was used, namely HBL-100 (breast), HeLa (cervix), SW-1573 (non-small cell lung) and WiDr (colon). A conformation-independent representation of the chemical structure was established in order to avoid leading with the scarce experimental information on X-ray crystal structure of the drug interaction. The 1179 theoretical descriptors derived with E-Dragon and Recon software were simultaneously analyzed through linear regression models based on the Replacement Method variable subset selection technique. The established models were validated and tested through the use of external test sets of compounds, the Leave-One-Out Cross Validation method, Y-Randomization and Applicability Domain analysis.

QSPR study on refractive indices of solvents commonly used in polymer chemistry using flexible molecular descriptors.

A predictive Quantitative Structure–Property Relationship (QSPR) for the refractive indices of 370 solvents commonly used in the processing and analysis of polymers is presented, using as chemical information descriptors the simplified molecular input line entry system (SMILES). The model employs a flexible molecular descriptor and a conformation-independent approach. Various well-known techniques, such as the use of an external test set of compounds, the cross-validation method, and Y-randomization were used to test and validate the established equations. The predicted values were finally compared with published results from the literature. The simple model proposed correlates the refractive index values with good accuracy, and it is not dependent on 3D-molecular geometries.

Predicting the bioconcentration factor through a conformation-independent QSPR study

Abstract The ANTARES dataset is a large collection of known and verified experimental bioconcentration factor data, involving 851 highly heterogeneous compounds from which 159 are pesticides. The BCF ANTARES data were used to derive a conformation-independent QSPR model. A large set of 27,017 molecular descriptors was explored, with the main intention of capturing the most relevant structural characteristics affecting the studied property. The structural descriptors were derived with different freeware tools, such as PaDEL, Epi Suite, CORAL, Mold2, RECON, and QuBiLs-MAS, and so it was interesting to find out the way that the different descriptor tools complemented each other in order to improve the statistical quality of the established QSPR. The best multivariable linear regression models were found with the Replacement Method variable sub-set selection technique. The proposed QSPR model improves previous reported models of the bioconcentration factor in the present dataset.

Different encoding alternatives for the prediction of halogenated polymers glass transition temperature by quantitative structure-property relationships

ABSTRACT The glass transition temperature, Tg, is one of the most important properties of amorphous polymers. The ability to predict the Tg value of a polymer preceding its synthesis is of enormous value. For this reason it is of great value to perform a predictive quantitative structure–property relationships analysis of Tg, in this case a new set of halogenated polymers was used for this purpose. In addition, to corroborate our previous findings, the best way to encode the polymers structure for this type of studies was further tested finding that the optimal option is once more to use three monomeric units. The best linear model constructed from 153 molecular structures incorporated seven molecular descriptors and showed excellent predictive ability. Furthermore, the method showed to be very simple and straightforward for the prediction of Tg since three-dimensional descriptors are not required.

QSAR studies of indoyl aryl sulfides and sulfones as reverse transcriptase inhibitors

The inhibitory HIV reverse transcriptase activity of 172 non-nucleoside indoyl aryl sulfones and sulfides is studied with a QSAR analysis, in order to identify the molecular characteristics influencing the interaction with the reverse transcriptase enzyme. This work increases the available QSAR studies of indoyl aryl sulfones and sulfides using the reported experimental EC50 values against HIV-1 wild type (IIIB) in human T-lymphocyte (CEM) cells. Different approaches are proposed, involving 0D, 1D and 2D molecular descriptors from PaDEL freeware, and also based on flexible descriptors from CORAL freeware. Three models are finally presented, which correlate the inhibitory HIV reverse transcriptase activity with good accuracy. It is demonstrated that the established models are predictive in the validation process. The novelty of the present work relies on the development of structure-inhibitory HIV activity relationships, through a computational technique that does not require the knowledge of the molecular conformation during the structural representation. The obtained results would contribute to guide the design of more effective compounds for HIV treatment.

The conformation-independent QSPR approach for predicting the oxidation rate constant of water micropollutants

In advanced water treatment processes, the degradation efficiency of contaminants depends on the reactivity of the hydroxyl radical toward a target micropollutant. The present study predicts the hydroxyl radical rate constant in water (kOH) for 118 emerging micropollutants, by means of quantitative structure-property relationships (QSPR). The conformation-independent QSPR approach is employed, together with a large number of 15,251 molecular descriptors derived with the PaDEL, Epi Suite, and Mold2 freewares. The best multivariable linear regression (MLR) models are found with the replacement method variable subset selection technique. The proposed five-descriptor model has the following statistics for the training set: Rtrain2=0.88

QSPR Studies on Refractive Indices of Structurally Heterogeneous Polymers

{R}_{\mathrm{train}}^2=0.88

Conformation-Independent QSAR Study on Human Epidermal Growth Factor Receptor-2 (HER2) Inhibitors

, RMStrain = 0.21, while for the test set is Rtest2=0.87