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Journal of Chemical Information and Modeling

Shufang Yang, Ran Wang, Guang Wan, Zhimin Wu, Shujuan Guo, Xingxing Dai, Xinyuan Shi, Yanjiang Qiao
Menthol is a widely used penetration enhancer in clinical medicine due to its high efficiency and relative safety. However, details of the penetration enhancement mechanism of menthol on molecular level is rarely involved in the discussion. In this work, the penetration enhancement (PE) mechanism of menthol is explored by a multiscale method containing molecular dynamics simulations, in vitro penetration experiments and transmission electron microscopy. Osthole is chosen to be the tested drug due to its common-using in external preparations and its representative-accompanying with menthol as a PE in the preparations...
October 21, 2016: Journal of Chemical Information and Modeling
Robert P Sheridan
Several papers have appeared in which a ligand efficiency index instead of pIC50 is used as the activity in QSAR. The claim is that better fits and predictions are obtained with ligand efficiency. We show that the apparent superiority is a statistical artifact due to the fact that ligand efficiency indices are more or less correlated with the physical property included in their definition (number of non-hydrogens, ALOGP, TPSA, etc.), and that the property is easier to predict than the original pIC50.
October 21, 2016: Journal of Chemical Information and Modeling
Jonathan David Tyzack, Peter A Hunt, Matthew D Segall
We describe methods for predicting Cytochrome P450 (CYP) metabolism incorporating both pathway-specific reactivity and isoform-specific accessibility considerations. Semi-empirical quantum mechanical (QM) simulations, parameterized using experimental data and ab initio calculations, estimate the reactivity of each potential site of metabolism in the context of the whole molecule. Ligand-based models, trained using high quality regioselectivity data, correct for orientation and steric effects of the different CYP isoform binding pockets...
October 18, 2016: Journal of Chemical Information and Modeling
James McDonagh, David S Palmer, Tanja Van Mourik, John B O Mitchell
We compare a range of computational methods for the prediction of sublimation thermodynamics (enthalpy, entropy and free energy of sublimation). These include a model from theoretical chemistry that utilizes crystal lattice energy minimization (with the DMACRYS program) and QSPR models generated by both machine learning (Random Forest and Support Vector Machines) and regression (Partial Least Squares) methods. Using these methods we investigate the predictability of the enthalpy, entropy and free energy of sublimation, with consideration of whether such a method may be able to improve solubility prediction schemes...
October 17, 2016: Journal of Chemical Information and Modeling
Peter Sadowski, David R Fooshee, Niranjan Subrahmanya, Pierre Baldi
We demonstrate how machine learning (ML) and quantum mechanical (QM) methods can be used in two-way synergy to build chemical reaction expert systems. The proposed ML approach to reaction prediction identifies electron sources and sinks among the reactants, and then ranks all source-sink pairs. This is used to address a major bottleneck of QM calculations by providing a prioritized list of mechanistic reaction steps. QM modeling can then be used to compute the transition states and activation energies of the top-ranked reactions, providing additional or improved examples of ranked source-sink pairs...
October 17, 2016: Journal of Chemical Information and Modeling
Magdalena Anna Mozolewska, Pawel Krupa, Bartlomiej Zaborowski, Adam Liwo, Jooyoung Lee, Keehyoung Joo, Cezary Czaplewski
Recently, we developed a new approach to protein-structure prediction, which combines template-based modeling with the physics-based coarse-grained UNited RESidue (UNRES) force field. In this approach, restrained multiplexed replica exchange molecular dynamics (MREMD) simulations with UNRES, with the C(α)-distance and virtual-bond-dihedral-angle restraints derived from knowledge-based models are carried out. In this work, we report a test of this approach in the 11th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP11), in which we used the template-based models from early-stage predictions by the LEE group CASP11 server (group 038, called 'nns'), and further improvement of the method...
October 17, 2016: Journal of Chemical Information and Modeling
Laura Guasch, Waruna Yapamudiyansel, Megan L Peach, James A Kelley, Joseph J Barchi, Marc C Nicklaus
We investigated how many cases of the same chemical sold as different products (at possibly different prices) occurred in a prototypical large aggregated database and simultaneously tested the tautomerism definitions in the chemoinformatics toolkit CACTVS. We applied the standard CACTVS tautomeric transforms plus a set of recently developed ring-chain transforms to the Aldrich Market Select (AMS) database of 6 million screening samples and building blocks. In 30 000 cases, two or more AMS products were found to be just different tautomeric forms of the same compound...
October 16, 2016: Journal of Chemical Information and Modeling
Sara Khan, Umar Farooq, Maria Kurnikova
In the present studies, we analyzed the influence of temperature on the stability and dynamics of the α subunit of tryptophan synthase (TRPS) from hyperthermophilic, mesophilic, and psychrophilic homologues at different temperatures by molecular dynamics simulations. Employing different indicators such as root-mean-square deviations, root-mean-square fluctuations, principal component analysis, and free energy landscapes, this study manifests the diverse behavior of these homologues with changes in temperature...
October 14, 2016: Journal of Chemical Information and Modeling
Elena Dolgikh, Ian A Watson, Prashant V Desai, Geri A Sawada, Stuart Morton, Timothy M Jones, Thomas J Raub
We report development and prospective validation of a QSAR model of the unbound brain-to-plasma partition coefficient, Kp,uu,brain, based on the in-house data set of ∼1000 compounds. We discuss effects of experimental variability, explore the applicability of both regression and classification approaches, and evaluate a novel, model-within-a-model approach of including P-glycoprotein efflux prediction as an additional variable. When tested on an independent test set of 91 internal compounds, incorporation of P-glycoprotein efflux information significantly improves the model performance resulting in an R(2) of 0...
October 13, 2016: Journal of Chemical Information and Modeling
Qin Wang, Simone Sciabola, Gabriela Barreiro, Xinjun Hou, Guoyun Bai, Michael J Shapiro, Frank E Koehn, Anabella Villalobos, Matthew P Jacobson
Macrocycles pose challenges for computer-aided drug design due to their conformational complexity. One fundamental challenge is identifying all low-energy conformations of the macrocyclic ring, which is important for modeling target binding, passive membrane permeation, and other conformation-dependent properties. Macrocyclic polyketides are medically and biologically important natural products characterized by structural and functional diversity. Advances in synthetic biology and semi-synthetic methods may enable creation of an even more diverse set of non-natural product polyketides for drug discovery and other applications...
October 12, 2016: Journal of Chemical Information and Modeling
Govindan Subramanian, Bharath Ramsundar, Vijay Pande, Rajiah Aldrin Denny
The binding affinities (IC50) reported for diverse structural and chemical classes of human β-secretase 1 (BACE-1) inhibitors in literature were modeled using multiple in silico ligand based modeling approaches and statistical techniques. The descriptor space encompasses simple binary molecular fingerprint, one- and two-dimensional constitutional, physicochemical, and topological descriptors, and sophisticated three-dimensional molecular fields that require appropriate structural alignments of varied chemical scaffolds in one universal chemical space...
October 10, 2016: Journal of Chemical Information and Modeling
Yu Zhang, Lirong Wang, Zhiwei Feng, Haizi Cheng, Terence Francis McGuire, Yahui Ding, Tao Cheng, Yingdai Gao, Xiang-Qun Xie
Given the capacity of self-renewal and multilineage differentiation, stem cells are promising sources for use in regenerative medicines as well as in the clinical treatment of certain hematological malignancies and degenerative diseases. Complex networks of cellular signaling pathways largely determine stem cell fate and function. Small molecules that modulate these pathways can provide important biological and pharmacological insights. However, it is still challenging to identify the specific protein targets of these compounds, to explore the changes in stem cell phenotypes induced by compound treatment and to ascertain compound mechanisms of action...
October 7, 2016: Journal of Chemical Information and Modeling
Matthew C Swain, Jacqueline M Cole
The emergence of "big data" initiatives has led to the need for tools that can automatically extract valuable chemical information from large volumes of unstructured data, such as the scientific literature. Since chemical information can be present in figures, tables, and textual paragraphs, successful information extraction often depends on the ability to interpret all of these domains simultaneously. We present a complete toolkit for the automated extraction of chemical entities and their associated properties, measurements, and relationships from scientific documents that can be used to populate structured chemical databases...
October 6, 2016: Journal of Chemical Information and Modeling
Athanasia-Panagiota Serafeim, Georgios Salamanos, Kalliopi K Patapati, Nicholas M Glykos
We examine the sensitivity of folding molecular dynamics simulations on the choice between three variants of the same force field (the AMBER99SB force field and its ILDN, NMR-ILDN, and STAR-ILDN variants). Using two different peptide systems (a marginally stable helical peptide and a β-hairpin) and a grand total of more than 20 μs of simulation time we show that even relatively minor force field changes can lead to appreciable differences in the peptide folding behavior.
October 5, 2016: Journal of Chemical Information and Modeling
Stavros Chatzieleftheriou, Matthew R Adendorff, Nikos D Lagaros
The potential energy of molecules and nanostructures is commonly calculated in the molecular mechanics formalism by superimposing bonded and nonbonded atomic energy terms, i.e. bonds between two atoms, bond angles involving three atoms, dihedral angles involving four atoms, nonbonded terms expressing the Coulomb and Lennard-Jones interactions, etc. In this work a new, generalized numerical simulation is presented for studying the mechanical behavior of three-dimensional nanostructures at the atomic scale. The energy gradient and Hessian matrix of such assemblies are usually computed numerically; a potential energy finite element model is proposed herein where these two components are expressed analytically...
October 5, 2016: Journal of Chemical Information and Modeling
Haiyang Zhang, Chunhua Yin, Hai Yan, David van der Spoel
Binding affinity prediction with implicit solvent models remains a challenge in virtual screening for drug discovery. In order to assess the predictive power of implicit solvent models in docking techniques with Amber scoring, three generalized Born models (GB(HCT), GB(OBC)I, and GB(OBC)II) available in Dock 6.7 were utilized, for determining the binding affinity of a large set of β-cyclodextrin complexes with 75 neutral guest molecules. The results were compared to potential of mean force (PMF) free energy calculations with four GB models (GB(Still), GB(HCT), GB(OBC)I, and GB(OBC)II) and to experimental data...
October 4, 2016: Journal of Chemical Information and Modeling
Tavina L Offutt, Robert V Swift, Rommie E Amaro
In silico virtual screening (VS) is a powerful hit identification technique used in drug discovery projects that aims to effectively distinguish true actives from inactive or decoy molecules. To better capture the dynamic behavior of protein drug targets, compound databases may be screened against an ensemble of protein conformations, which may be experimentally determined or generated computationally, i.e. via molecular dynamics (MD) simulations. Several studies have shown that conformations generated by MD are useful in identifying novel hit compounds, in part because structural rearrangements sampled during MD can provide novel targetable areas...
October 3, 2016: Journal of Chemical Information and Modeling
Mengdan Qian, Yaming Shan, Shanshan Guan, Hao Zhang, Song Wang, Weiwei Han
Protein tyrosine phosphatase 1B (PTP1B) has become an outstanding target for the treatment of diabetes and obesity. Recent research has demonstrated that some fullerene derivatives serve as a new nanoscale-class of potent inhibitors of PTP1B, but the specific mechanism remains unclear. Several molecular modeling methods (molecular docking, molecular dynamics simulations, and molecular mechanics/generalized Born surface area calculations) were integrated to provide insight into the binding mode and inhibitory mechanism of the new class of fullerene inhibitors...
October 3, 2016: Journal of Chemical Information and Modeling
Brandall L Ingle, Brandon C Veber, John W Nichols, Rogelio Tornero-Velez
The free fraction of a xenobiotic in plasma (Fub) is an important determinant of chemical adsorption, distribution, metabolism, elimination, and toxicity, yet experimental plasma protein binding data are scarce for environmentally relevant chemicals. The presented work explores the merit of utilizing available pharmaceutical data to predict Fub for environmentally relevant chemicals via machine learning techniques. Quantitative structure-activity relationship (QSAR) models were constructed with k nearest neighbors (kNN), support vector machines (SVM), and random forest (RF) machine learning algorithms from a training set of 1045 pharmaceuticals...
September 29, 2016: Journal of Chemical Information and Modeling
Paul Czodrowski, Wolf-Guido Bolick
The prediction of molecular targets is highly beneficial during the drug discovery process, be it for off-target elucidation or deconvolution of phenotypic screens. Here, we present OCEAN, a target prediction tool exclusively utilizing publically available ChEMBL data. OCEAN uses a heuristics approach based on a validation set containing almost 1000 drug ← → target relationships. New ChEMBL data (ChEMBL20 as well as ChEMBL21) released after the validation was used for a prospective OCEAN performance check...
September 26, 2016: Journal of Chemical Information and Modeling
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