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https://www.readbyqxmd.com/read/28549311/reaction-of-diazepam-and-related-benzodiazepines-with-chlorine-kinetics-transformation-products-and-in-silico-toxicological-assessment
#1
Inmaculada Carpinteiro, Rosario Rodil, José Benito Quintana, Rafael Cela
In this work, the reaction of four benzodiazepines (diazepam, oxazepam, nordazepam and temazepam) during water chlorination was studied by means of liquid chromatography-quadrupole-time of flight-mass spectrometry (LC-QTOF-MS). For those compounds that showed a significant degradation, i.e. diazepam, oxazepam and nordazepam, parameters affecting to the reaction kinetics (pH, chlorine and bromide level) were studied in detail and transformation products were tentatively identified. The oxidation reactions followed pseudofirst-order kinetics with rate constants in the range of 1...
May 3, 2017: Water Research
https://www.readbyqxmd.com/read/28546583/hybridizing-feature-selection-and-feature-learning-approaches-in-qsar-modeling-for-drug-discovery
#2
Ignacio Ponzoni, Víctor Sebastián-Pérez, Carlos Requena-Triguero, Carlos Roca, María J Martínez, Fiorella Cravero, Mónica F Díaz, Juan A Páez, Ramón Gómez Arrayás, Javier Adrio, Nuria E Campillo
Quantitative structure-activity relationship modeling using machine learning techniques constitutes a complex computational problem, where the identification of the most informative molecular descriptors for predicting a specific target property plays a critical role. Two main general approaches can be used for this modeling procedure: feature selection and feature learning. In this paper, a performance comparative study of two state-of-art methods related to these two approaches is carried out. In particular, regression and classification models for three different issues are inferred using both methods under different experimental scenarios: two drug-like properties, such as blood-brain-barrier and human intestinal absorption, and enantiomeric excess, as a measurement of purity used for chiral substances...
May 25, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28545350/utilization-of-the-monte-carlo-method-to-build-up-qsar-models-for-hemolysis-and-cytotoxicity-of-antimicrobial-peptides
#3
Alla P Toropova, Andrey A Toropov, Marten Beeg, Marco Gobbi, Mario Salmona
BACKGROUND: Traditional quantitative structure - property / activity relationships (QSPRs/QSARs) are based on representation of molecular structure by molecular graph or simplified molecular input-line entry system (SMILES). It is attractive idea to develop predictive models for large molecules in general and for peptides in particular. However, the representation of these molecules by molecular graph or SMILES is problematic owing to large size of these molecules. A possible alternative of SMILES is representation of peptides via sequence of abbreviations of amino acids...
May 24, 2017: Current Drug Discovery Technologies
https://www.readbyqxmd.com/read/28544873/3d-qsar-studies-of-some-reversible-acetyl-cholinesterase-inhibitors-based-on-comfa-and-ligand-protein-interaction-fingerprints-using-pc-ls-svm-and-pls-ls-svm
#4
Hamidreza Ghafouri, Mohsen Ranjbar, Amirhossein Sakhteman
A great challenge in medicinal chemistry is to develop different methods for structural design based on the pattern of the previously synthesized compounds. In this study two different QSAR methods were established and compared for a series of piperidine acetylcholinesterase inhibitors. In one novel approach, PC-LS-SVM and PLS-LS-SVM was used for modeling 3D interaction descriptors, and in the other method the same nonlinear techniques were used to build QSAR equations based on field descriptors. Different validation methods were used to evaluate the models and the results revealed the more applicability and predictive ability of the model generated by field descriptors (Q(2)LOO-CV=1, R(2)ext=0...
May 10, 2017: Computational Biology and Chemistry
https://www.readbyqxmd.com/read/28544552/kinome-wide-profiling-prediction-of-small-molecules
#5
Frieda Anna Sorgenfrei, Simone Fulle, Benjamin Merget
Extensive kinase profiling data, covering more than half of the human kinome, are nowadays available and allow construction of activity prediction models of high practical use. Proteochemometric (PCM) approaches utilize compound and protein descriptors, which enables the extrapolation of bioactivity values also to so far unexplored kinases. In this study, the potential of PCM to make large-scale predictions on the entire kinome is explored, considering the applicability on novel compounds and kinases, including clinically relevant mutants...
May 23, 2017: ChemMedChem
https://www.readbyqxmd.com/read/28542505/alzhcpi-a-knowledge-base-for-predicting-chemical-protein-interactions-towards-alzheimer-s-disease
#6
Jiansong Fang, Ling Wang, Yecheng Li, Wenwen Lian, Xiaocong Pang, Hong Wang, Dongsheng Yuan, Qi Wang, Ai-Lin Liu, Guan-Hua Du
Alzheimer's disease (AD) is a complicated progressive neurodegeneration disorder. To confront AD, scientists are searching for multi-target-directed ligands (MTDLs) to delay disease progression. The in silico prediction of chemical-protein interactions (CPI) can accelerate target identification and drug discovery. Previously, we developed 100 binary classifiers to predict the CPI for 25 key targets against AD using the multi-target quantitative structure-activity relationship (mt-QSAR) method. In this investigation, we aimed to apply the mt-QSAR method to enlarge the model library to predict CPI towards AD...
2017: PloS One
https://www.readbyqxmd.com/read/28539063/a-qsar-classification-model-for-neuraminidase-inhibitors-of-influenza-a-viruses-h1n1-based-on-weighted-penalized-support-vector-machine
#7
Z Y Algamal, M K Qasim, H T M Ali
Descriptor selection is a procedure widely used in chemometrics. The aim is to select the best subset of descriptors relevant to the quantitative structure-activity relationship (QSAR) study being considered. In this paper, a new descriptor selection method for the QSAR classification model is proposed by adding a new weight inside L1-norm. The experimental results from classifying the neuraminidase inhibitors of influenza A viruses (H1N1) demonstrate that the proposed method in the QSAR classification model performs effectively and competitively compared with other existing penalized methods in terms of classification performance and the number of selected descriptors...
May 25, 2017: SAR and QSAR in Environmental Research
https://www.readbyqxmd.com/read/28537140/molecular-modeling-driven-approach-for-identification-of-janus-kinase-1-inhibitors-through-3d-qsar-docking-and-molecular-dynamics-simulations
#8
Ramesh Itteboina, Srilata Ballu, Sree Kanth Sivan, Vijjulatha Manga
Janus kinase 1 (JAK 1) belongs to the JAK family of intracellular nonreceptor tyrosine kinase. JAK-signal transducer and activator of transcription (JAK-STAT) pathway mediate signaling by cytokines, which control survival, proliferation and differentiation of a variety of cells. Three-dimensional quantitative structure activity relationship (3 D-QSAR), molecular docking and molecular dynamics (MD) methods was carried out on a dataset of Janus kinase 1(JAK 1) inhibitors. Ligands were constructed and docked into the active site of protein using GLIDE 5...
May 24, 2017: Journal of Receptor and Signal Transduction Research
https://www.readbyqxmd.com/read/28532662/molecular-design-of-flotation-collectors-a-recent-progress
#9
REVIEW
Guangyi Liu, Xianglin Yang, Hong Zhong
The nature of froth flotation is to selectively hydrophobize valuable minerals by collector adsorption so that the hydrophobized mineral particles can attach air bubbles. In recent years, the increasing commercial production of refractory complex ores has been urgent to develop special collectors for enhancing flotation separation efficiency of valuable minerals from these ores. Molecular design methods offer an effective way for understanding the structure-property relationship of flotation collectors and developing new ones...
May 10, 2017: Advances in Colloid and Interface Science
https://www.readbyqxmd.com/read/28530546/the-role-of-qsar-and-virtual-screening-studies-in-type-2-diabetes-drug-discovery
#10
Simone Q Pantaleão, Drielli G V Fujji, Vinícius G Maltarollo, Danielle da C Silva, Gustavo H G Trossini, Karen C Weber, Luis P B Scott, Kathia M Honorio
BACKGROUND: Due to the increasing number of diabetes cases worldwide, there is an international concern to provide even more effective treatments to control this condition. METHODS: This review brings together a selection of studies that helped to broaden the comprehension of various biological targets and associated mechanisms involved in type 2 diabetes mellitus. RESULTS: Such studies demonstrated that QSAR techniques and virtual screenings have been successfully employed in drug design projects...
May 22, 2017: Medicinal Chemistry
https://www.readbyqxmd.com/read/28529482/mode-of-action-analyses-of-neferine-a-bisbenzylisoquinoline-alkaloid-of-lotus-nelumbo-nucifera-against-multidrug-resistant-tumor-cells
#11
Onat Kadioglu, Betty Y K Law, Simon W F Mok, Su-Wei Xu, Thomas Efferth, Vincent K W Wong
Neferine, a bisbenzylisoquinoline alkaloid isolated from the green seed embryos of Lotus (Nelumbo nucifera Gaertn), has been previously shown to have various anti-cancer effects. In the present study, we evaluated the effect of neferine in terms of P-glycoprotein (P-gp) inhibition via in vitro cytotoxicity assays, R123 uptake assays in drug-resistant cancer cells, in silico molecular docking analysis on human P-gp and in silico absorption, distribution, metabolism, and excretion (ADME), quantitative structure activity relationships (QSAR) and toxicity analyses...
2017: Frontiers in Pharmacology
https://www.readbyqxmd.com/read/28522333/development-of-qsars-for-parameterizing-physiology-based-toxicokinetic-models
#12
Dimosthenis Α Sarigiannis, Krystalia Papadaki, Periklis Kontoroupis, Spyros P Karakitsios
A Quantitative Structure Activity Relationship (QSAR) model was developed in order to predict physicochemical and biochemical properties of industrial chemicals of various groups. This model was based on the solvation equation, originally proposed by Abraham. In this work Abraham's solvation model got parameterized using artificial intelligence techniques such as artificial neural networks (ANNs) for the prediction of partitioning into kidney, heart, adipose, liver, muscle, brain and lung for the estimation of the bodyweight-normalized maximal metabolic velocity (Vmax) and the Michaelis - Menten constant (Km)...
May 15, 2017: Food and Chemical Toxicology
https://www.readbyqxmd.com/read/28521603/in-silico-binding-mechanism-prediction-of-benzimidazole-based-corticotropin-releasing-factor-1-receptor-antagonists-by-quantitative-structure-activity-relationship-molecular-docking-and-pharmacokinetic-parameters-calculation
#13
Neeraj Kumar, Shashank Shekhar Mishra, Chandra Shekhar Sharma, Hamendra Pratap Singh, Sourav Kalra
Despite the various research efforts towards the treatment of stress related disorders, the drug has not yet launched last 20 years. Corticotropin releasing factor-1 receptor antagonists have been point of great interest in stress related disorders. In the present study, we have selected benzazole scaffold based compounds as corticotropin releasing factor-1 antagonists and performed 2D and 3D QSAR studies to identify the structural features to elucidating the binding mechanism prediction. The best 2D QSAR model was obtained through multiple linear regression method with r(2) value of 0...
May 19, 2017: Journal of Biomolecular Structure & Dynamics
https://www.readbyqxmd.com/read/28521600/quantitative-structure-activity-relationship-study-of-amide-mosquito-repellents
#14
P Wang, X Xu, S Liao, J Song, G Fan, S Chen, Z Wang
A quantitative structure-activity relationship (QSAR) study on 43 amide repellents was carried out by the heuristic method in order to reveal the correlations between molecular parameters of these amides and their repellency against Aedes aegypti. Sketches and optimizations of molecular structures were achieved by the Gaussian software package. Generation and screening of molecular parameters were accomplished using CODESSA 2.7.10 software. The leave-one-out method was applied for the model validation. The results showed that a four-descriptor QSAR model with r(2) of 0...
April 2017: SAR and QSAR in Environmental Research
https://www.readbyqxmd.com/read/28521054/in-silico-prediction-of-drug-induced-liver-injury-based-on-adverse-drug-reaction-reports
#15
Xiang-Wei Zhu, Shao-Jing Li
Drug-induced liver injury (DILI) is a major cause of drug attrition. Currently existing Quantitative Structure-Activity Relationship (QSAR) models have limited predictive capabilities for DILI. Furthermore, their practical applications were limited by lack of new hepatotoxicity data. In this study, we first collected and curated a novel set of 122 DILI-positive and 932 DILI-negative drugs from online adverse drug reports using proportional reporting ratios as the signal detection method. Second, three strategies (under-sampling the majority class, synthetic minority over-sampling technique, and adjusting decision threshold approach) were employed to develop predictive classification models to cope with the unbalanced dataset...
May 17, 2017: Toxicological Sciences: An Official Journal of the Society of Toxicology
https://www.readbyqxmd.com/read/28513147/four-specific-hapten-conformations-dominating-antibody-specificity-quantitative-structure-activity-relationship-analysis-for-quinolone-immunoassay
#16
Jiahong Chen, Lanteng Wang, Lanlan Lu, Xing Shen, Xinan Huang, Yingju Liu, Xiulan Sun, Zhanhui Wang, Sergei Alexandrovich Eremin, Yuanming Sun, Zhenlin Xu, Hongtao Lei
Antibody-based immunoassay methods have been important tools for monitoring drug residues in animal foods. However, due to limited knowledge of the quantitative structure-activity relationship between a hapten and its resultant antibody specificity, it is still a huge challenge for antibody production with a desired specificity. In this study, the three-dimensional quantitative structure-activity relationship (3D QSAR) was analyzed in accordance with the cross-reactivity of quinolone drugs reacting with the antibody raised by pipemidic acid as the immunizing hapten, as well as comparing with the reported cross-reactivity data and their hapten structures...
May 17, 2017: Analytical Chemistry
https://www.readbyqxmd.com/read/28509592/molecular-modeling-and-structure-activity-relationships-for-a-series-of-benzimidazole-derivatives-as-cruzain-inhibitors
#17
Ivani Pauli, Leonardo G Ferreira, Mariana L de Souza, Glaucius Oliva, Rafaela S Ferreira, Marco A Dessoy, Brian W Slafer, Luiz C Dias, Adriano D Andricopulo
AIM: Chagas disease is endemic in Latin America and no effective treatment is available. Efforts in drug research have focused on several enzymes from Trypanosoma cruzi, among which cruzain is a validated pharmacological target. METHODOLOGY: Chemometric analyses were performed on the data set using the HQSAR, CoMFA and CoMSIA methods. Docking simulations were executed using the crystallographic structure of cruzain in complex with a benzimidazole inhibitor. The top-scoring enzyme-inhibitor complexes were selected for the development of the 3D QSAR models and to assess the inhibitor binding modes and intermolecular interactions...
May 16, 2017: Future Medicinal Chemistry
https://www.readbyqxmd.com/read/28503546/computational-tool-for-fast-in-silico-evaluation-of-herg-k-channel-affinity
#18
Giulia Chemi, Sandra Gemma, Giuseppe Campiani, Simone Brogi, Stefania Butini, Margherita Brindisi
The development of a novel comprehensive approach for the prediction of hERG activity is herein presented. Software Phase has been used to derive a 3D-QSAR model, employing as alignment rule a common pharmacophore built on a subset of 22 highly active compounds (threshold Ki: 50 nM) against hERG K(+) channel. Five features comprised the pharmacophore: two aromatic rings (R1 and R2), one hydrogen-bond acceptor (A), one hydrophobic site (H), and one positive ionizable function (P). The sequential 3D-QSAR model developed with a set of 421 compounds (randomly divided in training and test set) yielded a test set (Q(2)) = 0...
2017: Frontiers in Chemistry
https://www.readbyqxmd.com/read/28503093/alarms-about-structural-alerts
#19
Vinicius Alves, Eugene Muratov, Stephen Capuzzi, Regina Politi, Yen Low, Rodolpho Braga, Alexey V Zakharov, Alexander Sedykh, Elena Mokshyna, Sherif Farag, Carolina Andrade, Victor Kuz'min, Denis Fourches, Alexander Tropsha
Structural alerts are widely accepted in chemical toxicology and regulatory decision support as a simple and transparent means to flag potential chemical hazards or group compounds into categories for read-across. However, there has been a growing concern that alerts disproportionally flag too many chemicals as toxic, which questions their reliability as toxicity markers. Conversely, the rigorously developed and properly validated statistical QSAR models can accurately and reliably predict the toxicity of a chemical; however, their use in regulatory toxicology has been hampered by the lack of transparency and interpretability...
August 21, 2016: Green Chemistry: An International Journal and Green Chemistry Resource: GC
https://www.readbyqxmd.com/read/28501513/qsar-studies-of-the-bioactivity-of-hepatitis-c-virus-hcv-ns3-4a-protease-inhibitors-by-multiple-linear-regression-mlr-and-support-vector-machine-svm
#20
Zijian Qin, Maolin Wang, Aixia Yan
In this study, quantitative structure-activity relationship (QSAR) models using various descriptor sets and training/test set selection methods were explored to predict the bioactivity of hepatitis C virus (HCV) NS3/4A protease inhibitors by using a multiple linear regression (MLR) and a support vector machine (SVM) method. 512 HCV NS3/4A protease inhibitors and their IC50 values which were determined by the same FRET assay were collected from the reported literature to build a dataset. All the inhibitors were represented with selected nine global and 12 2D property-weighted autocorrelation descriptors calculated from the program CORINA Symphony...
May 3, 2017: Bioorganic & Medicinal Chemistry Letters
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