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SAR and QSAR in Environmental Research

A Furuhama, T I Hayashi, N Tatarazako
We constructed models for acute to chronic estimation of the Daphnia magna reproductive toxicities of chemical substances from their Daphnia magna acute immobilization toxicities. The models combined the acute toxicities with structural and physicochemical descriptors. We used multiregression analysis and selected the descriptors for the models by means of a genetic algorithm. Of the best 100 models (as indicated by the lack of fit score), 90% included the following descriptors: acute toxicity (i.e. an activity parameter), distribution coefficient (log D) and structural indicator variables that indicate the presence of -NH2 attached to aromatic carbon and the presence of a chlorine atom...
October 21, 2016: SAR and QSAR in Environmental Research
S A Kulkarni, E Benfenati, T S Barton-Maclaren
One of the key challenges of Canada's Chemicals Management Plan (CMP) is assessing chemicals with limited/no empirical hazard data for their risk to human health. In some instances, these chemicals have not been tested broadly for their toxicological potency; as such, limited information exists on their potential to induce human health effects following exposure. Although (quantitative) structure activity relationship ((Q)SAR) models are able to generate predictions to address data gaps for certain toxicological endpoints, the confidence in predictions also needs to be addressed...
October 20, 2016: SAR and QSAR in Environmental Research
Y Liu, L Huang, H Ye, X Lv
Interferon regulatory factor-7 (IRF-7) is involved in pulmonary infection and pneumonia. Here, a synthetic strategy that combined quantitative structure-activity relationship (QSAR)-based virtual screening and in vitro binding assay was described to identify new and potent mediator ligands of IRF-7 from natural products. In the procedure, a QSAR scoring function was developed and validated using Gaussian process (GP) regression and a structure-based set of protein-ligand affinity data. By integrating hotspot pocket prediction, pharmacokinetics profile analysis and molecular docking calculations, the scoring function was successfully applied to virtual screening against a large library of structurally diverse, drug-like natural products...
October 20, 2016: SAR and QSAR in Environmental Research
Mare Oja, Uko Maran
Human intestinal absorption is a key property for orally administered drugs and is dependent on pH. This study focuses on neutral and amphoteric compounds and their membrane permeabilities across the range of pH values found in the human intestine. The membrane permeability values for 15 neutral and 60 amphoteric compounds at pH 3, 5, 7.4 and 9 were measured using the parallel artificial membrane permeability assay (PAMPA). For each data series the quantitative structure-permeability relationships were developed and analysed...
October 17, 2016: SAR and QSAR in Environmental Research
W Liu, J Wang, M Li, W Tang, J Han
We previously developed a lutein-polyvinylpyrrolidone (PVP) complex with improved aqueous saturation solubility and stability, though the conjugation mechanism is still unclear. In this paper, experiments with astaxanthin-PVP complex and curcumin-PVP complex were carried out, which indicated that PVP could improve the solubility and stability of astaxanthin and curcumin. We aimed to construct a computational model capable of understanding the protective effect in complexes formed between PVP and antioxidants, through which the binding mode of PVP and antioxidants was investigated with molecular modelling in order to obtain the interactions, binding energy, binding site and surface area between PVP and antioxidants...
October 17, 2016: SAR and QSAR in Environmental Research
W F C Rocha, D A Sheen
The ability to determine the biodegradability of chemicals without resorting to expensive tests is ecologically and economically desirable. Models based on quantitative structure-activity relations (QSAR) provide some promise in this direction. However, QSAR models in the literature rarely provide uncertainty estimates in more detail than aggregated statistics such as the sensitivity and specificity of the model's predictions. Almost never is there a means of assessing the uncertainty in an individual prediction...
October 6, 2016: SAR and QSAR in Environmental Research
C R García-Jacas, Y Marrero-Ponce, S J Barigye, T Hernández-Ortega, L Cabrera-Leyva, A Fernández-Castillo
Novel N-tuple topological/geometric cutoffs to consider specific inter-atomic relations in the QuBiLS-MIDAS framework are introduced in this manuscript. These molecular cutoffs permit the taking into account of relations between more than two atoms by using (dis-)similarity multi-metrics and the concepts related with topological and Euclidean-geometric distances. To this end, the kth two-, three- and four-tuple topological and geometric neighbourhood quotient (NQ) total (or local-fragment) spatial-(dis)similarity matrices are defined, to represent 3D information corresponding to the relations between two, three and four atoms of the molecular structures that satisfy certain cutoff criteria...
October 6, 2016: SAR and QSAR in Environmental Research
H R Xu, L Fu, P Zhan, X Y Liu
In this study, we retrieved a series of 59 dihydroalkylthio-benzyloxopyrimidine (S-DABO) derivatives, which is a class of highly potent HIV-1 non-nucleoside reverse transcriptase inhibitors (NNRTIs) reported from published articles, and analysed them with comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Statistically significant three-dimensional quantitative structure-activity relationship (3D-QSAR) models by CoMFA and CoMSIA were derived from a training set of 46 compounds on the basis of the rigid body alignment...
September 26, 2016: SAR and QSAR in Environmental Research
H Golmohammadi, Z Dashtbozorgi
In the present work, enhanced replacement method (ERM) and support vector machine (SVM) were used for quantitative structure-property relationship (QSPR) studies of polarity parameter (p) of various organic compounds in methanol in reversed phase liquid chromatography based on molecular descriptors calculated from the optimized structures. Diverse kinds of molecular descriptors were calculated to encode the molecular structures of compounds, such as geometric, thermodynamic, electrostatic and quantum mechanical descriptors...
September 23, 2016: SAR and QSAR in Environmental Research
T-D Ngo, T-D Tran, M-T Le, K-M Thai
The efflux pumps P-glycoprotein (P-gp) in humans and NorA in Staphylococcus aureus are of great interest for medicinal chemists because of their important roles in multidrug resistance (MDR). The high polyspecificity as well as the unavailability of high-resolution X-ray crystal structures of these transmembrane proteins lead us to combining ligand-based approaches, which in the case of this study were machine learning, perceptual mapping and pharmacophore modelling. For P-gp inhibitory activity, individual models were developed using different machine learning algorithms and subsequently combined into an ensemble model which showed a good discrimination between inhibitors and noninhibitors (acctrain-diverse = 84%; accinternal-test = 92% and accexternal-test = 100%)...
September 2016: SAR and QSAR in Environmental Research
Y Y Ren, L C Zhou, L Yang, P Y Liu, B W Zhao, H X Liu
The paper highlights the use of the logistic regression (LR) method in the construction of acceptable statistically significant, robust and predictive models for the classification of chemicals according to their aquatic toxic modes of action. Essentials accounting for a reliable model were all considered carefully. The model predictors were selected by stepwise forward discriminant analysis (LDA) from a combined pool of experimental data and chemical structure-based descriptors calculated by the CODESSA and DRAGON software packages...
September 2016: SAR and QSAR in Environmental Research
Z Y Algamal, M H Lee, A M Al-Fakih, M Aziz
In high-dimensional quantitative structure-activity relationship (QSAR) modelling, penalization methods have been a popular choice to simultaneously address molecular descriptor selection and QSAR model estimation. In this study, a penalized linear regression model with L1/2-norm is proposed. Furthermore, the local linear approximation algorithm is utilized to avoid the non-convexity of the proposed method. The potential applicability of the proposed method is tested on several benchmark data sets. Compared with other commonly used penalized methods, the proposed method can not only obtain the best predictive ability, but also provide an easily interpretable QSAR model...
September 2016: SAR and QSAR in Environmental Research
T S Whiteside, S H Hilal, A Brenner, L A Carreira
The entropy of fusion, enthalpy of fusion, and melting point of organic compounds can be estimated through three models developed using the SPARC (SPARC Performs Automated Reasoning in Chemistry) platform. The entropy of fusion is modelled through a combination of interaction terms and physical descriptors. The enthalpy of fusion is modelled as a function of the entropy of fusion, boiling point, and flexibility of the molecule. The melting point model is the enthalpy of fusion divided by the entropy of fusion...
August 2016: SAR and QSAR in Environmental Research
A R Kennicutt, L Morkowchuk, M Krein, C M Breneman, J E Kilduff
A quantitative structure-activity relationship was developed to predict the efficacy of carbon adsorption as a control technology for endocrine-disrupting compounds, pharmaceuticals, and components of personal care products, as a tool for water quality professionals to protect public health. Here, we expand previous work to investigate a broad spectrum of molecular descriptors including subdivided surface areas, adjacency and distance matrix descriptors, electrostatic partial charges, potential energy descriptors, conformation-dependent charge descriptors, and Transferable Atom Equivalent (TAE) descriptors that characterize the regional electronic properties of molecules...
August 2016: SAR and QSAR in Environmental Research
L Sun, S Meng
The human proton-coupled peptide transporter (hPEPT1) with broad substrates is an important route for improving the pharmacokinetic performance of drugs. Thus, it is essential to predict the affinity constant between drug molecule and hPEPT1 for rapid virtual screening of hPEPT1's substrate during lead optimization, candidate selection and hPEPT1 prodrug design. Here, a structure-based in silico model for 114 compounds was constructed based on eight structural parameters. This model was built by the multiple linear regression method and satisfied all the prerequisites of the regression models...
August 2016: SAR and QSAR in Environmental Research
O A Raevsky, D E Polianczyk, A Mukhametov, V Y Grigorev
Assessment of "CNS drugs/CNS candidates" classification abilities of the multi-parametric optimization (CNS MPO) approach was performed by logistic regression. It was found that the five out of the six separately used physical-chemical properties (topological polar surface area, number of hydrogen-bonded donor atoms, basicity, lipophilicity of compound in neutral form and at pH = 7.4) provided accuracy of recognition below 60%. Only the descriptor of molecular weight (MW) could correctly classify two-thirds of the studied compounds...
August 2016: SAR and QSAR in Environmental Research
H Moghadam, M Rahgozar, S Gharaghani
Prediction of drug-disease associations is one of the current fields in drug repositioning that has turned into a challenging topic in pharmaceutical science. Several available computational methods use network-based and machine learning approaches to reposition old drugs for new indications. However, they often ignore features of drugs and diseases as well as the priority and importance of each feature, relation, or interactions between features and the degree of uncertainty. When predicting unknown drug-disease interactions there are diverse data sources and multiple features available that can provide more accurate and reliable results...
August 2016: SAR and QSAR in Environmental Research
T Tibaut, J Borišek, M Novič, D Turk
Autolysin E (AtlE) is a bacteriolytic enzyme which plays an important role in division and growth of bacterial cells and therefore represents a promising potential drug target. Its 3D structure has been recently elucidated. We used in silico prediction tools to study substrate or ligand (inhibitor) binding regions of AtlE. We applied several freely available tools and a commercial tool for binding site identification and compared results of the prediction. Calculation time, number of predictions and output data provided by specific software vary according to the different approaches utilized by specific method categories...
July 2016: SAR and QSAR in Environmental Research
S Gupta, N Basant, D Mohan, K P Singh
Experimental determinations of the rate constants of the reaction of NO3 with a large number of organic chemicals are tedious, and time and resource intensive; and the development of computational methods has widely been advocated. In this study, we have developed room-temperature (298 K) and temperature-dependent quantitative structure-reactivity relationship (QSRR) models based on the ensemble learning approaches (decision tree forest (DTF) and decision treeboost (DTB)) for predicting the rate constant of the reaction of NO3 radicals with diverse organic chemicals, under OECD guidelines...
July 2016: SAR and QSAR in Environmental Research
X-W Zhu, Y-J Xin, Q-H Chen
In this study, recursive random forests were used to build classification models for mouse liver toxicity. The mouse liver toxicity endpoint (67 toxic and 166 non-toxic) was a composition of four in vivo chronic systemic and carcinogenic toxicity endpoints (non-proliferative, neoplastic, proliferative and gross pathology). A multiple under-sampling approach and a shifted classification threshold of 0.288 (non-toxic < 0.288 and toxic ≥ 0.288) were used to cope with the unbalanced data. Our study showed that recursive random forests are very efficient in variable selection and for the development of predictive in silico models...
July 2016: SAR and QSAR in Environmental Research
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