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Cover Picture: (Mol. Inf. 5/2012)
Molecular Informatics publishes research that will deepen our understanding about information storage and processing on the molecular level, signaling and regulation of biological and chemical systems including cellular systems and macromolecular assemblies, modeling of molecular interactions and networks, and the design of molecular modulators that exhibit desired biochemical and pharmacological effects. Various aspects of this transdisciplinary scientific area are depicted on the cover: Cells with their nuclei and membranes (image courtesy of Dr. A. Schreiner and E. Resch), models of receptor-ligand interactions, and an artistic representation of “biological information” as multiple bit-codes presented on a right-handed helix. |
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Disease-Specific Differentiation Between Drugs and Non-Drugs Using Principal Component Analysis of Their Molecular Descriptor Space
AbstractThe physicochemical descriptor space has been extensively mapped and described in the literature for orally administered drugs and lead compounds. However, consideration of negative examples (non-drugs) or disease pathophysiology is not common in many studies. In the present work, a principal component analysis was carried out using drugs and non-drugs taking into account disease- and organ-specific categories, as well as different administration routes in addition to oral. The study involves 1386 relevant small-molecules including natural and synthetic products. Drug-specific as well as disease-category-specific or organ-specific regions and their respective threshold sets (ranges of descriptors) relative to non-drugs were elucidated on the scores plot and validated with external, independent sets of drugs and non-drugs. The respective loadings plot of molecular descriptors was rationalized in terms of physicochemically relevant groups related to the components of solvation free energy. The results of this analysis can contribute to the improved profiling of drug candidates and libraries making use of disease- and organ-specificity coded by physicochemical descriptors and ligand binding efficiency. 
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Networks of ProteinProtein Interactions: From Uncertainty to Molecular Details
AbstractProteins are the bricks and mortar of cells. The work of proteins is structural and functional, as they are the principal element of the organization of the cell architecture, but they also play a relevant role in its metabolism and regulation. To perform all these functions, proteins need to interact with each other and with other bio-molecules, either to form complexes or to recognize precise targets of their action. For instance, a particular transcription factor may activate one gene or another depending on its interactions with other proteins and not only with DNA. Hence, the ability of a protein to interact with other bio-molecules, and the partners they have at each particular time and location can be crucial to characterize the role of a protein. Proteins rarely act alone; they rather constitute a mingled network of physical interactions or other types of relationships (such as metabolic and regulatory) or signaling cascades. In this context, understanding the function of a protein implies to recognize the members of its neighborhood and to grasp how they associate, both at the systemic and atomic level. The network of physical interactions between the proteins of a system, cell or organism, is defined as the interactome. The purpose of this review is to deepen the description of interactomes at different levels of detail: from the molecular structure of complexes to the global topology of the network of interactions. The approaches and techniques applied experimentally and computationally to attain each level are depicted. The limits of each technique and its integration into a model network, the challenges and actual problems of completeness of an interactome, and the reliability of the interactions are reviewed and summarized. Finally, the application of the current knowledge of protein-protein interactions on modern network medicine and protein function annotation is also explored. 
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Prospective Prediction of Antitarget Activity by Matched Molecular Pairs Analysis
AbstractMatched molecular pairs analysis (MMPA)1,2 is an inverse quantitative structure activity relationship (QSAR) technique that is rapidly gaining popularity in the retrospective analysis of large experimental datasets.3,4 While much of the recent focus has been on the differences in properties between structurally related groups of existing compounds, attempts to extend this methodology to the de-novo design of novel structures have been limited. To our knowledge the aggregate effect of multiple transformations, all suggesting the same molecular structure, has only ever being considered within a very limited dataset.5 We therefore sought to test this exciting new approach to the design (and absolute property prediction – effectively QSAR-by-MMPA) of novel chemical entities based on a larger, more diverse dataset, and couple these designs to MMPA-based predictions of antitarget activity. 
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A Novel QSPR Model for Prediction of Gas to Dimethyl Sulfoxide Solvation Enthalpy of Organic Compounds Based on Support Vector Machine
AbstractIn this study, a quantitative structure![[BOND]](http://onlinelibrarystatic.wiley.com/undisplayable_characters/00f8ff.gif) property relationship (QSPR) study is developed for the prediction of gas to dimethyl sulfoxide solvation enthalpy (ΔHSolv) of organic compounds based on molecular descriptors calculated solely from molecular structure considerations. Diverse types of molecular descriptors were calculated to represent the molecular structures of the various compounds studied. Multiple linear regression (MLR) was employed to select an optimal subset of descriptors that have significant contributions to the ΔHSolv overall property. Our investigation revealed that the dependence of physicochemical properties on solvation enthalpy is a nonlinear observable fact and that MLR method is unable to model the solvation enthalpy accurately. It has been observed that support vector machine (SVM) and artificial neural network (ANN) demonstrates better performance compared with MLR. The standard error value of the test set for SVM is 1.731 kJ mol−1, while it is 2.303 kJ mol−1 and 5.146 kJ mol−1 for ANN and MLR, respectively. The results showed that the calculated ΔHSolv values by SVM were in good agreement with the experimental data, and the performance of the SVM model was superior to those of MLR and ANN ones. 
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Orthogonal PLS (OPLS) Modeling for Improved Analysis and Interpretation in Drug Design
AbstractPartial least squares (PLS) regression is a flexible data analytical approach, which can be made even more versatile and useful by various modifications. In this article we describe the extension into orthogonal PLS modeling, in terms of two new methods, called OPLS and O2PLS, with similar prediction capacity but improved model interpretation. |
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Molecular Modelling of G Protein-Coupled Receptors Through the Web
AbstractWith the recent crystallization of several G Protein-Coupled receptors (GPCRs), homology modelling and all atom molecular dynamics (MD) simulations have proven their usefulness for exploring the structure and function of this superfamily of membrane receptors. Subsequently, automated computational protocols have been implemented as web-based servers in the recent years to produce reliable models of GPCRs, providing partial or global solutions for the structural characterization and molecular simulation of GPCRs. These dedicated modelling services represent an attractive tool for the broader community of public researchers and pharmaceutical companies, in order to assist in the structure-based drug design of GPCRs. We here collect and analyze the existing web servers, among which a previously unreported service, GPCR-ModSim, offers for the first time full atom MD simulations in the pipeline for GPCR molecular modelling. 
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