QSAR modeling of chemical penetration enhancers using novel replacement algorithms

The applications of transdermal delivery are limited because of the resistance of the skin to drug diffusion. Only potent drugs, with molecular weight less than 500 Da, are suitable to cross the skin barrier. Chemical Penetration Enhancers (CPEs) are used to promote the absorption of solutes across the dermal layers. In this investigation, a Quantitative Structure-Activity Relationship (QSAR) model is applied to relate chemical penetration enhancer structures with the flux enhancement ratio through a statistical approach. A database, consisting of 61 non-polar CPEs, is selected for the study. Each compound is represented by 777 QSAR descriptors, which encode the physical characteristics of the CPE and its structure. Selection replacement techniques are used to choose the eight most important descriptors. The enhancement ratio, an evaluation of the effect of the CPE, correlates well with this subset of features. The QSAR model can be adopted to predict factors that need to be adjusted to improve permeation of the drug through the skin. Three QSAR models are developed using different algorithms: forward stepwise regression (FSR), replacement (RM) and enhanced replacement (ERM) techniques. The first two methods yield equations with poor predictive power. The enhanced replacement method gives the best results, which meet cross-validation criteria: q2 = 0.79, 0.63 and 0.76 for the training set, test set and combined data, respectively. These results meet the predetermined criteria

QSAR analysis for the inhibition of the mutagenic activity by anthocyanin derivatives

Flavonoid compounds modulate the cytochrome P450 3A4 enzyme activity and inhibit the mutagenic activity of mammalian cells, preventing carcinogen activation and cellular DNA damage. In this work, the quantitative structure-activity relationships (QSAR) theory is applied to predict the cytochrome P450 3A4 inhibition constant by anthocyanin derivatives. Different freely available software calculates 102,260 non-conformational molecular descriptors. A training set of 12 compounds is used to calibrate the best univariable linear regression models, while a test set of 4 compounds is used to explore their predictive capability. The present results are compared with previously reported ones by using 3D-QSAR, thus demonstrating that the proposed topological QSAR models achieve acceptable statistical quality. The proposed model provides a prospective QSAR guide for the search of new anthocyanin derivatives possessing high or low predicted mutagenicity.Fil: Szewczuk, Nicolas Alejadro. 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: Pomilio, Alicia Beatriz. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Bioquímica Clínica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

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: R2 train = 0:88, RMStrain = 0.21, while for the test set is R2 test = 0:87, RMStest = 0.11. This QSPR serves as a rational guide for predicting oxidation processes of micropollutants.Instituto de Investigaciones Fisicoquímicas Teóricas y AplicadasFacultad de Ciencias Agrarias y Forestale

Linear Regression QSAR Models for Polo-Like Kinase-1 Inhibitors

A structurally diverse dataset of 530 polo-like kinase-1 (PLK1) inhibitors is compiled from the ChEMBL database and studied by means of a conformation-independent quantitative structure-activity relationship (QSAR) approach. A large number (26,761) of molecular descriptors are explored with the main intention of capturing the most relevant structural characteristics affecting the bioactivity. The structural descriptors are derived with different freeware, such as PaDEL, Mold2, and QuBiLs-MAS; such descriptor software complements each other and improves the QSAR results. The best multivariable linear regression models are found with the replacement method variable subset selection technique. The balanced subsets method partitions the dataset into training, validation, and test sets. It is found that the proposed linear QSAR model improves previously reported models by leading to a simpler alternative structure-activity relationship.Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicada

QSAR predictions on antichagas fenarimols

A useful QSAR model was developed to predict the antichagas activity for 760 fenarimol analogues obtained from the ChEMBL database, which are considered as very active and selective inhibitors of Trypanosoma cruzi. Various molecular descriptor programs provided a large number of 67,116 non-conformational molecular descriptors that were analyzed through multivariable linear regressions and the Replacement Method technique. Through THESE descriptors, the quantification of the structure–activity relationship achieves an acceptable statistical quality for compounds having experimental activity. The present work provides a prospective guide for predicting the inhibitory activity against T. cruzi of structurally-related fenarimol compounds.Fil: 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: Fioressi, Silvina Ethel. Universidad de Belgrano. Facultad de Ciencias Exactas y Naturales. Departamento de Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Bacelo, Daniel Enrique. Universidad de Belgrano. Facultad de Ciencias Exactas y Naturales. Departamento de Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Prediction of Drug-Likeness Using Deep Autoencoder Neural Networks

Due to diverse reasons, most drug candidates cannot eventually become marketed drugs. Developing reliable computational methods for prediction of drug-likeness of candidate compounds is of vital importance to improve the success rate of drug discovery and development. In this study, we used a fully connected neural networks (FNN) to construct drug-likeness classification models with deep autoencoder to initialize model parameters. We collected datasets of drugs (represented by ZINC World Drug), bioactive molecules (represented by MDDR and WDI), and common molecules (represented by ZINC All Purchasable and ACD). Compounds were encoded with MOLD2 two-dimensional structure descriptors. The classification accuracies of drug-like/non-drug-like model are 91.04% on WDI/ACD databases, and 91.20% on MDDR/ZINC, respectively. The performance of the models outperforms previously reported models. In addition, we develop a drug/non-drug-like model (ZINC World Drug vs. ZINC All Purchasable), which distinguishes drugs and common compounds, with a classification accuracy of 96.99%. Our work shows that by using high-latitude molecular descriptors, we can apply deep learning technology to establish state-of-the-art drug-likeness prediction models

Development of Estrogen Receptor Beta Binding Prediction Model Using Large Sets of Chemicals

We developed an ERβ binding prediction model to facilitate identification of chemicals specifically bind ERβ or ERα together with our previously developed ERα binding model. Decision Forest was used to train ERβ binding prediction model based on a large set of compounds obtained from EADB. Model performance was estimated through 1000 iterations of 5-fold cross validations. Prediction confidence was analyzed using predictions from the cross validations. Informative chemical features for ERβ binding were identified through analysis of the frequency data of chemical descriptors used in the models in the 5-fold cross validations. 1000 permutations were conducted to assess the chance correlation. The average accuracy of 5-fold cross validations was 93.14% with a standard deviation of 0.64%. Prediction confidence analysis indicated that the higher the prediction confidence the more accurate the predictions. Permutation testing results revealed that the prediction model is unlikely generated by chance. Eighteen informative descriptors were identified to be important to ERβ binding prediction. Application of the prediction model to the data from ToxCast project yielded very high sensitivity of 90-92%. Our results demonstrated ERβ binding of chemicals could be accurately predicted using the developed model. Coupling with our previously developed ERα prediction model, this model could be expected to facilitate drug development through identification of chemicals that specifically bind ERβ or ERα

QSAR predictions on antichagas fenarimols

A useful QSAR model was developed to predict the antichagas activity for 760 fenarimol analogues obtained from the ChEMBL database, which are considered as very active and selective inhibitors of Trypanosoma cruzi. Various molecular descriptor programs provided a large number of 67,116 non-conformational molecular descriptors that were analyzed through multivariable linear regressions and the Replacement Method technique. Through THESE descriptors, the quantification of the structure–activity relationship achieves an acceptable statistical quality for compounds having experimental activity. The present work provides a prospective guide for predicting the inhibitory activity against T. cruzi of structurally-related fenarimol compounds.Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicada