QSAR study for carcinogenicity in a large set of organic compounds

In our continuing efforts to find out acceptable Absorption, Distribution, Metabolization, Elimination and Toxicity (ADMET) properties of organic compounds, we establish linear QSAR models for the carcinogenic potential prediction of 1464 compounds taken from the "Galvez data set", that include many marketed drugs. More than a thousand of geometry-independent molecular descriptors are simultaneously analyzed, obtained with the softwares E-Dragon and Recon. The variable subset selection method employed is the Replacement Method, and also the improved version Enhanced Replacement Method. The established models are properly validated through an external test set of compounds, and by means of the Leave-Group-Out Cross Validation method. In addition, we apply the Y-Randomization strategy and analyze the Applicability Domain of the developed model. Finally, we compare the results obtained in present study with the previous ones from the literature. The novelty of present work relies on the development of an alternative predictive structure-carcinogenicity relationship in a large heterogeneous set of organic compounds, by only using a reduced number of geometry independent molecular descriptors.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: Comelli, Nieves Carolina. Universidad Nacional de Catamarca. Facultad de Ciencias Agrarias; ArgentinaFil: Ortiz, Erlinda del Valle. Universidad Nacional de Catamarca. Facultad de Tecnología y Ciencias 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

Investigation on Quantitative Structure-Activity Relationships of 1,3,4 Oxadiazole Derivatives as Potential Telomerase Inhibitors

The published manuscript is available at EurekaSelect via http://www.eurekaselect.com/164022/article, DOI : 10.2174/1570163815666180724113208. © 2018 Bentham ScienceA series of 1,3,4-oxadiazole derivatives with significant broad-spectrum anticancer activity against different cell lines, and demonstrated telomerase inhibition, was subjected to Quantitative Structure-Activity Relationships (QSAR) analysis. Validated models with high correlation coefficients were developed. The Multiple Linear Regression (MLR) models, by Ordinary Least Squares (OLS), showed good robustness and predictive capability, according to the Multi-Criteria Decision Making (MCDM = 0.8352), a technique that simultaneously enhances the performances of a certain number of criteria. The descriptors selected for the models, such as electrotopological state (E-state) descriptors, and extended topochemical atom (ETA) descriptors, showed the relevant chemical information contributing to the activity of these compounds. The results obtained in this study make sure about the identification of potential hits as prospective telomerase inhibitors.Peer reviewedFinal Accepted Versio

tackling malaria

Malaria is an infectious disease that affects over 216 million people worldwide, killing over 445,000 patients annually. Due to the constant emergence of parasitic resistance to the current antimalarial drugs, the discovery of new drug candidates is a major global health priority. Aiming to make the drug discovery processes faster and less expensive, we developed binary and continuous Quantitative Structure-Activity Relationships (QSAR) models implementing deep learning for predicting antiplasmodial activity and cytotoxicity of untested compounds. Then, we applied the best models for a virtual screening of a large database of chemical compounds. The top computational predictions were evaluated experimentally against asexual blood stages of both sensitive and multi-drug-resistant Plasmodium falciparum strains. Among them, two compounds, LabMol-149 and LabMol-152, showed potent antiplasmodial activity at low nanomolar concentrations (EC50 <500 nM) and low cytotoxicity in mammalian cells. Therefore, the computational approach employing deep learning developed here allowed us to discover two new families of potential next generation antimalarial agents, which are in compliance with the guidelines and criteria for antimalarial target candidates.publishersversionpublishe

QSPR Modeling of Bioconcentration Factors of Nonionic Organic Compounds

The terms bioaccumulation and bioconcentration refer to the uptake and build-up of chemicals that can occur in living organisms. Experimental measurement of bioconcentration is time-consuming and expensive, and is not feasible for a large number of chemicals of potential regulatory concern. A highly effective tool depending on a quantitative structure-property relationship (QSPR) can be utilized to describe the tendency of chemical concentration organisms represented by, the important ecotoxicological parameter, the logarithm of Bio Concentration Factor (log BCF) with molecular descriptors for a large set of non-ionic organic compounds. QSPR models were developed using multiple linear regression, partial least squares and neural networks analyses. Linear and non-linear QSPR models to predict log BCF of the compounds developed for the relevant descriptors. The results obtained offer good regression models having good prediction ability. The descriptors used in these models depend on the volume, connectivity, molar refractivity, surface tension and the presence of atoms accepting H-bonds

Prediction of physico-chemical properties for REACH based on QSPR models

International audienceQuantitative Structure Property Relationship models have been developed for the prediction of flash points of two families of organic compounds selected in the PREDIMOL French Project: amines and organic peroxides. If the model dedicated to amines respected all OECD validation principles with excellent performances in predictivity, the one dedicated to organic peroxides was not validated on an external validation set, due to the low number of available data, but already presented high performances in fitting and robustness. This work highlighted the need of gathering experimental data, as in progress in the PREDIMOL project, to achieve validated reliable models that could be used in a regulatory framework, like REACH. Such models are expected to be submitted to the European Joint Research Comity (JRC) and to existing tools (like the OECD ECHA QSAR Toolbox) to be available for use by industrials and regulatory instances

Poisson Parameters of Antimicrobial Activity: A Quantitative Structure-Activity Approach

A contingency of observed antimicrobial activities measured for several compounds vs. a series of bacteria was analyzed. A factor analysis revealed the existence of a certain probability distribution function of the antimicrobial activity. A quantitative structure-activity relationship analysis for the overall antimicrobial ability was conducted using the population statistics associated with identified probability distribution function. The antimicrobial activity proved to follow the Poisson distribution if just one factor varies (such as chemical compound or bacteria). The Poisson parameter estimating antimicrobial effect, giving both mean and variance of the antimicrobial activity, was used to develop structure-activity models describing the effect of compounds on bacteria and fungi species. Two approaches were employed to obtain the models, and for every approach, a model was selected, further investigated and found to be statistically significant. The best predictive model for antimicrobial effect on bacteria and fungi species was identified using graphical representation of observed vs. calculated values as well as several predictive power parameters

Consistency of QSAR models: Correct split of training and test sets, ranking of models and performance parameters

<div><p>Recent implementations of QSAR modelling software provide the user with numerous models and a wealth of information. In this work, we provide some guidance on how one should interpret the results of QSAR modelling, compare and assess the resulting models, and select the best and most consistent ones. Two QSAR datasets are applied as case studies for the comparison of model performance parameters and model selection methods. We demonstrate the capabilities of sum of ranking differences (SRD) in model selection and ranking, and identify the best performance indicators and models. While the exchange of the original training and (external) test sets does not affect the ranking of performance parameters, it provides improved models in certain cases (despite the lower number of molecules in the training set). Performance parameters for external validation are substantially separated from the other merits in SRD analyses, highlighting their value in data fusion.</p></div

In silico prediction of acute chemical toxicity of biocides in marine crustaceans using machine learning

Biocides are a heterogeneous group of chemical substances intended to control the growth or kill undesired organisms. Due to their extensive use, they enter marine ecosystems via non-point sources and may pose a threat to ecologically important non-target organisms. Consequently, industries and regulatory agencies have recognized the ecotoxicological hazard potential of biocides. However, the prediction of biocide chemical toxicity on marine crustaceans has not been previously evaluated. This study aims to provide in silico models capable of classifying structurally diverse biocidal chemicals into different toxicity categories and predict acute chemical toxicity (LC50) in marine crustaceans using a set of calculated 2D molecular descriptors. The models were built following the guidelines recommended by the OECD (Organization for Economic Cooperation and Development) and validated through stringent processes (internal and external validation). Six machine learning (ML) models were built and compared (linear regression: LR; support vector machine: SVM; random forest: RF; feed-forward backpropagation-based artificial neural network: ANN; decision trees: DT and naïve Bayes: NB) for regression and classification analysis to predict toxicities. All the models displayed encouraging results with high generalisability: the feed-forward-based backpropagation method showed the best results with determination coefficient R2 values of 0.82 and 0.94, respectively, for training set (TS) and validation set (VS). For classification-based modelling, the DT model performed the best with an accuracy (ACC) of 100 % and an area under curve (AUC) value of 1 for both TS and VS. These models showed the potential to replace animal testing for the chemical hazard assessment of untested biocides if they fall within the applicability domain of the proposed models. In general, the models are highly interpretable and robust, with good predictive performance. The models also displayed a trend indicating that toxicity is largely influenced by factors such as lipophilicity, branching, non-polar bonding and saturation of molecules