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Molecular Informatics

Alexey A Orlov, Evgeny V Khvatov, Alexander A Koruchekov, Anastasia A Nikitina, Anastasia D Zolotareva, Anastasia A Eletskaya, Liubov I Kozlovskaya, Vladimir A Palyulin, Dragos Horvath, Dmitry I Osolodkin, Alexandre Varnek
Recent outbreaks of dangerous viral infections, such as Ebola virus disease, Zika fever, etc., are forcing the search for new antiviral compounds. Preferably, such compounds should possess broad-spectrum antiviral activity, as the development of drugs for the treatment of dozens of viral infections lacking specific treatment would require significant resources. Antiviral activity data present in public resources are very sparse and further investigation of structure-activity relationships is necessary. One of the strategies could be the investigation of chemical space around known active compounds and assessment of activity against closely related viruses in order to fill in the antiviral activity matrix...
February 19, 2019: Molecular Informatics
Giuseppe Ermondi, Giulia Caron
Improving the interpretability of multivariate QSPR models is a major issue in modern drug discovery. In this study we applied three strategies to model and deconvolute the balance of intermolecular forces governing log KW SDS , a chromatographic descriptor of potential relevance in the prediction of ADME phenomena. A dataset of 77 compounds was set-up and an ad hoc pool of VS+ descriptors calculated. The data matrix was firstly submitted to a PCA run for a preliminary analysis and outliers detection. To model and interpret log KW SDS three chemoinformatic approaches implementing either variable selection or grouping tools were used: a) MLR and GA, b) PLSR combined with BR analysis and c) MBPLSR...
February 15, 2019: Molecular Informatics
Anita Sosnowska, Jakub Brzeski, Piotr Skurski, Tomasz Puzyn
The acidity of Lewis-Brønsted superacids can be derived from the theoretical calculations as the Gibbs free energy of the deprotonation reaction (ΔGacid ), which describes the tendency of a studied compound to donate a proton. This paper presents the first Quantitative Structure - Property Relationship (QSPR) model that correlates the ΔGacid of superacid (HF/MeX3 formula (X=F, Cl, Br)) with their structure. Developed model is well fitted, roubustness, has good predictive abilities, fulfills all OECD recommendation for good model...
February 12, 2019: Molecular Informatics
Elif Nagihan Kahraman, Melek Türker Saçan
Quantitative structure-toxicity relationship (QSTR) models were built for two in vitro endpoints: cytotoxicity and enzymatic activity of diverse chemicals to goldfish (Crassius auratus) scale tissue (GFS) and topminnow (Poeciliopsis lucida) hepatoma cell line (PLHC-1), respectively. The data sets were based on experimental cytotoxicity measured with uptake of 3-amino-7-dimethylamino-2-methylphenazine hydrochloride dye (Neutral Red assay) representing lysosomal damage and enzymatic activity measured with Ethoxyresorufin-O-deethylase (EROD) induction potency...
February 7, 2019: Molecular Informatics
El Hassen Mokrani, Abderrahmane Bensegueni, Ludovic Chaput, Claire Beauvineau, Hanane Djeghim, Liliane Mouawad
Acetylcholinesterase (AChE) is currently the most favorable target for the symptomatic treatment and reduction of Alzheimer's disease (AD). In order to identify new potent inhibitors of this enzyme, we describe herein a new structure-based virtual screening (SBVS) using the Institut Curie-CNRS chemical library (ICCL), which contained at the screening date 14307 compounds. The strategy undertaken in this work consisted of the use of several docking programs in SBVS calculations followed by the application of a consensus method (vSDC) and a scrupulous visual analysis...
February 6, 2019: Molecular Informatics
Alexandra Naß, David Schaller, Gerhard Wolber
For drug design projects it is essential to rationally induce and explain selectivity. In this context shape complementarity as well as protein and ligand flexibility represent important factors. Currently available tools for the analysis of protein-ligand interactions focus mainly on electrostatic complementarity and/or static structures. Here we address the shortcomings of available methods by presenting two new tools: The first one can be used to assess steric complementarity in flexible protein-ligand complexes in order to explain selectivity of known ligands...
February 6, 2019: Molecular Informatics
Luana de Morais E Silva, Vitor Prates Lorenzo, Wilton Silva Lopes, Luciana Scotti, Marcus Tullius Scotti
The increase of chemical pollutants detected in different aquatic environments over the past few years has been in the focus of several studies related to their occurrence, transport, fate, and hazards, or risks to human and environmental health. In Brazil, recent studies have been conducted on the occurrence of a series of organic micropollutants (OMPs) in aquatic environments. Nevertheless, the toxicological information and environmental behavior for most of these pollutants are still difficult to evaluate...
February 6, 2019: Molecular Informatics
Alla P Toropova, Andrey A Toropov
The CORAL software is a tool to build up predictive models for various endpoints by means of Quantitative Structure-Property/Activity Relationships (QSPRs/QSARs). A new criterion for assessment of the predictive potential of QSPR/QSAR models, so-called Index of Ideality of Correlation (IIC) is applied to improve the software. The ability of the IIC to detect models with better predictive potential is checked up with groups of random splits of data into the structured training set and extrenal validation set...
February 6, 2019: Molecular Informatics
Nikolay Kochev, Vesselina Paskaleva, Ognyan Pukalov, Nina Jeliazkova
Ambit-GCM is a new software tool for group contribution modelling (GCM), developed as a part of the chemoinformatics platform AMBIT. It is an open-source tool distributed under LGPL license, written in Java and based on the Chemistry Development Kit. Ambit-GCM provides an environment for creating models of molecular properties using additive schemes of zero, first or second orders. Ambit-GCM supports a set of local atomic attributes used for dynamic configuration of desired atom descriptions, which are applied to define fragments of different sizes...
January 17, 2019: Molecular Informatics
Guillaume Fayet, Patricia Rotureau
New Quantitative Structure-Property Relationships (QSPR) are presented to predict the flash point of binary liquid mixtures, based on more than 600 experimental flash points for 60 binary mixtures. Two models are proposed based on a GA-MLR approach that uses a genetic algorithm (GA) variable selection in multilinear regressions (MLR). In these models, mixtures were characterized by a series of mixture descriptors calculated from various mixture formula combining the molecular descriptors of the single compounds constituting the mixtures and their respective molar fractions in the mixture...
January 17, 2019: Molecular Informatics
James W Firman, Samuel J Belfield, George Chen, Megan Jackson, Fai Hou Lam, Callum Richmond, James Smith, Fabian P Steinmetz, Mark T D Cronin
Recent years have seen the emergence into circulation of a growing array of novel psychoactive substances (NPS). Knowledge of the pharmacological profiles and risk liability of these compounds is typically very scarce. Development of chemoinformatic tools enabling prediction of properties within uncharacterised analogues has potential be of particular use. In order to facilitate this, compilation of a chemical inventory comprising known NPS is a necessity. Sourcing a variety of published governmental and analytical reports, a dataset composed of 690 distinct acknowledged NPS, complete with defined chemical structures, has been constructed...
January 17, 2019: Molecular Informatics
Dusan Ruzic, Milos Petkovic, Danica Agbaba, A Ganesan, Katarina Nikolic
Histone deacetylase 6 (HDAC6) is unique hydrolase within HDAC family, having pleiotropic deacetylase activity against α-tubulin, cortactin and dynein. Comprehensively, HDAC6 controls cell motility, apoptosis and protein folding, whereas alterations in its structure and function are related to the pathogenesis of cancer, neurodegeneration and inflammation. To define structural motifs which guide HDAC6 selectivity, we developed and compared three-dimensional Quantitative Structure-Activity Relationship (3D-QSAR) models for HDAC1 and HDAC6 inhibitors...
January 11, 2019: Molecular Informatics
Luminita Crisan, Ana Borota, Takahiro Suzuki, Simona Funar-Timofei
Neonicotinoids are known to have high insecticidal potency, low mammalian toxicity and relatively tough activity for the development of resistance against aphids. A series of guadipyr insecticides, active against Myzus persicae was engaged in silico studies, based on Multiple Linear Regression (MLR), Partial Least Squares regression (PLS), Artificial Neural Networks (ANN), Support Vector Machine (SVM) and Pharmacophore modeling. Robust and predictive models were built using correlations between the insecticidal profile, expressed by experimental pLC50 values, and molecular descriptors, calculated from the energy optimized structures...
January 11, 2019: Molecular Informatics
Urmi Roy
The interactions between the tumor necrosis factor (TNF) and its receptor molecule are responsible for various signaling networks that are central to the functioning of human immune homeostasis. The present work is a computational study of certain structural aspects of this cell-signaling protein, specifically focusing on the molecular level analyses of the TNF receptor (TNF-R), guided by its crystallographic structure. We also examine the possible binding sites of the TNF onto TNF-R, and the associated interactions...
January 11, 2019: Molecular Informatics
Zhibin Du, Akbar Ali, Nenad Trinajstić
The modified first Zagreb connection index ( <mml:math xmlns:mml=""> <mml:mrow> <mml:mi>Z</mml:mi> <mml:msubsup> <mml:mi>C</mml:mi> <mml:mn>1</mml:mn> <mml:mo>*</mml:mo> </mml:msubsup> </mml:mrow> </mml:math> ) is a molecular descriptor, which was initially appeared within a formula of the total electron energy of alternant hydrocarbons in 1972. In a recent paper [A. Ali, N. Trinajstić, A novel/old modification of the first Zagreb index, Mol...
January 7, 2019: Molecular Informatics
Davide Ballabio, Francesca Grisoni, Viviana Consonni, Roberto Todeschini
The ICCVAM Acute Toxicity Workgroup (U.S. Department of Health and Human Services), in collaboration with the U.S. Environmental Protection Agency (U.S. EPA, National Center for Computational Toxicology), coordinated the "Predictive Models for Acute Oral Systemic Toxicity" collaborative project to develop in silico models to predict acute oral systemic toxicity for filling regulatory needs. In this framework, new Quantitative Structure-Activity Relationship (QSAR) models for the prediction of very toxic (LD50 lower than 50 mg/kg) and nontoxic (LD50 greater than or equal to 2,000 mg/kg) endpoints were developed, as described in this study...
December 14, 2018: Molecular Informatics
Chia-Hsiu Chen, Kenichi Tanaka, Kimito Funatsu
Quantitative structure-property relationships were developed to predict the liquid crystalline (LC) of a large dataset of aromatic organic compounds using machine learning algorithms and different molecular descriptors. The aim of this study was to find appropriate models and descriptors for the prediction of a large variety of liquid crystalline behaviors. Furthermore, descriptor calculations based on LC structural templates were proposed to understand the structural effects on the LC behaviors. The results suggest that random forest classifier and combined features which consists of structural templates were usable for LC behavior prediction...
December 7, 2018: Molecular Informatics
H Mirshahvalad, R Ghasemiasl, Nahid Raoufi, M Malekzadeh Dirin
The present study introduces a QSPR model to predict the flash point of pure organic compounds from diverse chemical families. We used the Maximum-Relevance Minimum-Redundancy (MRMR) as an efficient descriptor selection algorithm to select 20 the most effective out of 1926 calculated descriptors. The selected descriptors and their combination with the normal boiling point data were used as model inputs and their correlation with FP was mapped using feedforward artificial neural networks. Studying various models, the best result was obtained by a neural network with 2 neurons in the hidden layer for which a combination of the selected descriptors and normal boiling point data were used as model inputs...
November 29, 2018: Molecular Informatics
Sergey Sosnin, Mariia Vashurina, Michael Withnall, Pavel Karpov, Maxim Fedorov, Igor V Tetko
Despite the increasing volume of available data, the proportion of experimentally measured data remains small compared to the virtual chemical space of possible chemical structures. Therefore, there is a strong interest in simultaneously predicting different ADMET and biological properties of molecules, which are frequently strongly correlated with one another. Such joint data analyses can increase the accuracy of models by exploiting their common representation and identifying common features between individual properties...
November 28, 2018: Molecular Informatics
Gulcin Tugcu, Meric Koksal
In this publication, QSAR models were developed to predict analgesic and anti-inflammatory activities of some 2-benzoxazolinone derivatives using multiple linear regression method. The models were validated internally and externally according to the OECD principles. With the help of these models, pronounced molecular properties of these compounds related to activities were also explored. The developed models demonstrated that hydrophobicity, the number of halogens, and the shape of the molecular structure of these candidate drugs are prominent to represent analgesic and anti-inflammatory activities...
November 27, 2018: Molecular Informatics
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