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SAR and QSAR in Environmental Research

A Bak, V Kozik, I Malik, J Jampilek, A Smolinski
The current study examines in silico characterization of the structure-inhibitory potency for a set of phenylcarbamic acid derivatives containing an N-arylpiperazine scaffold, considering the electronic, steric and lipophilic properties. The main objective of the ligand-based modelling was the systematic study of classical comparative molecular field analysis (CoMFA)/comparative molecular surface analysis (CoMSA) performance for the modelling of in vitro efficiency observed for these phenylcarbamates, revealing their inhibitory activities against a virulent Mycobacterium tuberculosis H37 Rv strain...
September 19, 2018: SAR and QSAR in Environmental Research
A Furuhama, T I Hayashi, H Yamamoto
Herein, we propose models for predicting fish early-life stage (ELS) toxicity from acute Daphnia magna toxicity and various molecular descriptors. Specifically, eight models were developed with fathead minnow (Pimephales promelas) data and were validated against Japanese medaka (Oryzias latipes) data because the quantity of available Japanese medaka data is much smaller than the quantity of fathead minnow data. The training data set for the models consisted of ELS fathead minnow toxicity data for 77 chemicals; data for 67 of the 77 chemicals originated from the OPP Pesticide Ecotoxicity Database of the US Environmental Protection Agency...
September 5, 2018: SAR and QSAR in Environmental Research
T Tibaut, T Tomašič, V Hodnik, M Anderluh, S Pintar, M Novič, D Turk
A structure-based approach is applied for the development of inhibitors of bacterial N-acetyglucosaminidase (autolysin). Autolysins are enzymes involved in the degradation of peptidoglycan and therefore participate in bacterial cell growth and different lysis phenomena. Several studies indicate that by the inhibition of autolysins, and consequently of bacterial cell division, antibacterial activity can be obtained, thus paving the road to a novel group of therapeutics against human pathogens. As crystal structures of the autolysin E (AtlE)-ligand complexes were obtained in our laboratories, fragment-based virtual screening was the method of choice for the initial studies...
August 30, 2018: SAR and QSAR in Environmental Research
A Rácz, D Bajusz, K Héberger
Prediction performance often depends on the cross- and test validation protocols applied. Several combinations of different cross-validation variants and model-building techniques were used to reveal their complexity. Two case studies (acute toxicity data) were examined, applying five-fold cross-validation (with random, contiguous and Venetian blind forms) and leave-one-out cross-validation (CV). External test sets showed the effects and differences between the validation protocols. The models were generated with multiple linear regression (MLR), principal component regression (PCR), partial least squares (PLS) regression, artificial neural networks (ANN) and support vector machines (SVM)...
August 30, 2018: SAR and QSAR in Environmental Research
J Devillers
Repellents disrupt the behaviour of blood-seeking mosquitoes protecting humans against their bites which can transmit serious diseases. Since the mid-1950s, N,N-diethyl-m-toluamide (DEET) is considered as the standard mosquito repellent worldwide. However, DEET presents numerous shortcomings. Faced with the heightening risk of mosquito expansion caused by global climate changes and increasing international exchanges, there is an urgent need for a better repellent than DEET and the very few other commercialised repelling molecules such as picaridin and IR3535...
September 2018: SAR and QSAR in Environmental Research
K Venko, V Drgan, M Novič
Nowadays, environmental and biological endpoints can be predicted with in silico approaches if sufficient experimental data of good quality are available. Since the experimental evaluation of acute contact toxicity towards honeybees (Apis mellifera) is a complex and expensive assay, the computational models that follow OECD principles for this endpoint prediction represent important alternatives for safety prioritisation of chemicals, especially pesticides. We developed and validated counter-propagation artificial neural network (CPANN) models for in silico evaluation of toxicity of pesticides towards honeybees by using new in-house software...
September 2018: SAR and QSAR in Environmental Research
E Nagihan Kahraman, M Türker Saçan
Two data sets on the cytotoxicity of diverse chemicals to topminnow (Poeciliopsis lucida) hepatoma cell line (PLHC-1) were modelled with quantitative structure-toxicity relationship (QSTR). The data sets are based on 3-amino-7-dimethylamino-2-methylphenazine hydrochloride (NR) and 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assays representing lysosomal damage and metabolic impairment, respectively. The descriptors were calculated with DRAGON 6 and SPARTAN 10 software packages. Descriptor selection was made by 'all subset' and genetic algorithm-based features implemented in QSARINS software...
September 2018: SAR and QSAR in Environmental Research
J Devillers, A Larghi, C Lagneau
Space spraying of deltamethrin allows the control of adult Aedes (Stegomyia) aegypti mosquitoes. Unfortunately, many vector control programs are threatened by the development of resistances that decrease the efficacy of this adulticide. Faced with this situation, we can either try to use another insecticide presenting a different mechanism of action or find a strategy that brings back the efficacy of the insecticide at a satisfying level to pursue its use in vector control. Restoration of the efficacy of an insecticide can be obtained by means of a synergist...
August 2018: SAR and QSAR in Environmental Research
M Marzo, E Benfenati
Using data from the Leadscope database and Procter and Gamble researchers (1172 compounds after data curation) a new classification model to predict reproductive toxicity was developed. The model is based on Naïve Bayesian methods that use the fingerprint "extended connectivity fingerprint 2". Bits generated by the fingerprint are used from the models as descriptors to discriminate between the two classes. This technique permits the creation of a model without the use of descriptors. After a study on the probability scores, the Naïve Bayesian Fingerprint model shows a good performance on reproductive toxicity...
August 2018: SAR and QSAR in Environmental Research
M Vračko, S C Basak, F Witzmann
Applications of nanomaterials in biomedical, industrial, and consumer goods areas are expanding rapidly because of their unique physicochemical properties. Hazard assessment of nanosubstances is necessary for the protection of human and ecological health. We studied the proteomics patterns of three cell lines: co-culture of Caco-2 and HT29-MTX cells, primary small airway epithelial cells, and THP-1macrophage-like cells. The cells were exposed at 10 μg and 100 μg concentrations for 3 and 24 hours to multi-walled carbon nanotubes and TiO2 nanobelts (TiO2 -NB)...
August 2018: SAR and QSAR in Environmental Research
E Benfenati, A Golbamaki, G Raitano, A Roncaglioni, S Manganelli, F Lemke, U Norinder, Elena Lo Piparo, M Honma, A Manganaro, G Gini
Results from the Ames test are the first outcome considered to assess the possible mutagenicity of substances. Many QSAR models and structural alerts are available to predict this endpoint. From a regulatory point of view, the recommendation from international authorities is to consider the predictions of more than one model and to combine results in order to develop conclusions about the mutagenicity risk posed by chemicals. However, the results of those models are often conflicting, and the existing inconsistency in the predictions requires intelligent strategies to integrate them...
August 2018: SAR and QSAR in Environmental Research
S Majumdar, S C Basak, C N Lungu, M V Diudea, G D Grunwald
Quantitative bioactivity and toxicity assessment of chemical compounds plays a central role in drug discovery as it saves a substantial amount of resources. To this end, high-performance computing has enabled researchers and practitioners to leverage hundreds, or even thousands, of computed molecular descriptors for the activity prediction of candidate compounds. In this paper, we evaluate the utility of two large groups of chemical descriptors by such predictive modelling, as well as chemical structure discovery, through empirical analysis...
August 2018: SAR and QSAR in Environmental Research
V Stoičkov, S Šarić, M Golubović, D Zlatanović, D Krtinić, L Dinić, B Mladenović, D Sokolović, A M Veselinović
Angiotensin-converting enzyme (ACE) inhibitors have been acknowledged as first-line agents for the treatment of hypertension and a variety of cardiovascular disorders. In this context, quantitative structure-activity relationship (QSAR) models for a series of non-peptide compounds as ACE inhibitors are developed based on Simplified Molecular Input-Line Entry System (SMILES) notation and local graph invariants. Three random splits into the training and test sets are used. The Monte Carlo method is applied for model development...
July 2018: SAR and QSAR in Environmental Research
B Sepehri, M Rezaei, R Ghavami
To identify new HSP90 inhibitors, the ATP binding site of the N-domain of HSP90 was targeted by molecular docking of a library of 23,129,083 compounds (from the ZINC database) to the ATP binding site of the N-domain of HSP90. Structure-based virtual screen (SBVS) was performed using idock software on the istar web platform. Based on idock binding energies, 40 molecules were considered as HSP90 inhibitors. In the next step, the 40 molecules and the compound AT13387 (Onalespib) were docked to the XJX binding site using AutoDock Vina software...
July 2018: SAR and QSAR in Environmental Research
I Luque Ruiz, M Á Gómez-Nieto
The building of quantitative structure-activity relationship (QSAR) models for the in silico prediction of volume distribution for drugs at steady-state levels is vital for the selection of potential drugs at the synthesis stage. Using molecular descriptor matrixes, some regression models presenting low accuracy have been proposed, mainly due to the difficulty of compiling an appropriate dataset and the lack of information on dataset representation. In this paper, we use a benchmark dataset of very diverse drugs for the development of predictive models for volume distribution based on the use of relative distance matrixes as the input data to QSAR algorithms...
July 2018: SAR and QSAR in Environmental Research
M K Qasim, Z Y Algamal, H T Mohammad Ali
Quantitative structure-activity relationship (QSAR) classification modelling with descriptor selection has become increasingly important because of the existence of large datasets in terms of either the number of compounds or the number of descriptors. Descriptor selection can improve the accuracy of QSAR classification studies and reduce their computation complexity by removing the irrelevant and redundant descriptors. In this paper, a two-stage classification approach is proposed by combining the minimum redundancy maximum relevancy criterion with the sparse support vector machine...
July 2018: SAR and QSAR in Environmental Research
E Nazarshodeh, R Sheikhpour, S Gharaghani, M A Sarram
Carbonic anhydrases (CAs) are essential enzymes in biological processes. Prediction of the activity of compounds towards CA isoforms could be evaluated by computational techniques to discover a novel therapeutic inhibitor. Studies such as quantitative structure-activity relationships (QSARs), molecular docking and pharmacophore modelling have been carried out to design potent inhibitors. Unfortunately, QSAR does not consider the information of target space in the model. We successfully developed an in silico proteochemometrics model that simultaneously uses target and ligand descriptors to predict the activities of CA inhibitors...
June 2018: SAR and QSAR in Environmental Research
K Kranthi Kumar, B Uma Devi, P Neeraja
In this study, cholesterol biotransformation gene-set of human steroidogenic acute regulatory protein-related lipid transfer (START) domains were evaluated from high-throughput gene screening approaches. It was shown that STARD1, STARD3 and STARD4 proteins are better effective transporters of cholesterol than STARD5 and STARD6 domains. Docking studies show a strong agreement with gene ontology enrichment data. According to both complementary strategies, it was found that only STARD1, STARD3 and STARD4 are potentially involved in cholesterol biotransformation in mitochondria through Ω1-loop of C-terminal α4-helical domain...
June 2018: SAR and QSAR in Environmental Research
Y Liang, S Zhang, S Ding
Gram-negative bacterial secreted proteins play different roles in invaded eukaryotic cells and cause various diseases. Prediction of Gram-negative bacterial secreted protein types is a meaningful and challenging task. In this paper, we develop a multiple statistical features extraction model based on the dipeptide composition (DPC) descriptor and the detrended moving-average auto-cross-correlation analysis (DMACA) descriptor by PSI-BLAST profile. A 610-dimensional feature vector was constructed on the training set, and the feature extraction model was denoted DPC-DMACA-PSSM...
June 2018: SAR and QSAR in Environmental Research
J M Fitzpatrick, D W Roberts, G Patlewicz
Predictive testing to characterise substances for their skin sensitisation potential has historically been based on animal models such as the Local Lymph Node Assay (LLNA) and the Guinea Pig Maximisation Test (GPMT). In recent years, EU regulations, have provided a strong incentive to develop non-animal alternatives, such as expert systems software. Here we selected three different types of expert systems: VEGA (statistical), Derek Nexus (knowledge-based) and TIMES-SS (hybrid), and evaluated their performance using two large sets of animal data: one set of 1249 substances from eChemportal and a second set of 515 substances from NICEATM...
June 2018: SAR and QSAR in Environmental Research
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