449,897 research outputs found

    The literature of chemoinformatics : 1978–2018

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    This article presents a study of the literature of chemoinformatics, updating and building upon an analogous bibliometric investigation that was published in 2008. Data on outputs in the field, and citations to those outputs, were obtained by means of topic searches of the Web of Science Core Collection. The searches demonstrate that chemoinformatics is by now a well-defined sub-discipline of chemistry, and one that forms an essential part of the chemical educational curriculum. There are three core journals for the subject: The Journal of Chemical Information and Modeling, the Journal of Cheminformatics, and Molecular Informatics, and, having established itself, chemoinformatics is now starting to export knowledge to disciplines outside of chemistry

    Techniques for Galactic Dust Measurements in the Heliosphere

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    Galactic interstellar dust (ISD) is the major ingredient in planetary formation. However, information on this important material has been extremely limited. Recently the Ulysses dust detector has identified and measured interstellar dust outside 1.8~AU from the Sun at ecliptic latitudes above 5050^{\circ}. Inside this distance it could not reliably distinguish interstellar from interplanetary dust. Modeling the Ulysses data suggests that up to 30 % of dust flux with masses above 1016kg10^{-16}\rm kg at 1~AU is of interstellar origin. From the Hiten satellite in high eccentric orbit about the Earth there are indications that ISD indeed reaches the Earth's orbit. Two new missions carrying dust detectors, Cassini and Stardust, will greatly increase our observational knowledge. In this paper we briefly review instruments used on these missions and compare their capabilities. The Stardust mission [{\em Brownlee et al.}, 1996] will analyze the local interstellar dust population by an in-situ chemical analyzer and collect ISD between 2 and 3~AU from the Sun. The dust analyzer on the Cassini mission will determine the interstellar dust flux outside Venus' orbit and will provide also some compositional information. Techniques to identify the ISD flux levels at 1~AU are described that can quantify the interstellar dust flux in high-Earth orbit (outside the debris belts) and provide chemical composition information of galactic dust.Comment: Accepted for Journal of Geophysical Research, 6 figures, Late

    Modeling and Analysis of a Spectrum of the Globular Cluster NGC 2419

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    NGC 2419 is the most distant massive globular cluster in the outer Galactic halo. It is unusual also due to the chemical peculiarities found in its red giant stars in recent years. We study the stellar population of this unusual object using spectra obtained at the 1.93-m telescope of the Haute-Provence Observatory. At variance with commonly used methods of high-resolution spectroscopy applicable only to bright stars, we employ spectroscopic information on the integrated light of the cluster. We carry out population synthesis modeling of medium-resolution spectra using synthetic stellar atmosphere models based on a theoretical isochrone corresponding accurately to the observed color-magnitude diagram. We study the influence of non-Local Thermodynamic Equilibrium for some chemical elements on our results. The derived age (12.6 Gyr), [Fe/H]=-2.25 dex, helium content Y=0.25, and abundances of 12 other chemical elements are in a good qualitative agreement with published high-resolution spectroscopy estimates for red giant members in the cluster. On the other hand, the derived element abundance, [alpha/Fe]=0.13 dex (the mean of [O/Fe], [Mg/Fe] and [Ca/Fe]), differs from the published one ([alpha/Fe] =0.4 dex) for selected red giants in the cluster and may be explained by a large dispersion in the alpha-element abundances recently discovered in NGC2419. We suggest that studies of the {\it integrated} light in the cluster using high-resolution spectrographs in different wavelength regions will help to understand the nature of these chemical anomalies.Comment: 19 pages, 6 figures, accepted for publication in the journal "Astronomy Reports". This work was presented in a poster at IAU General Assembly XXVIII, Beijing 2012 (Special Session 1 "Origin and Complexity of Massive Star Clusters"). Four sentences were added thanks comments of Th. H. Puzi

    The Irony of Manganocene: An Interplay between the Jahn-Teller Effect and Close-Lying Electronic and Spin States

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    Although the unusual structural, magnetic,electronic, and spin characteristics of manganocene hasintrigued scientists for decades, a unified explanation andrationalization of its properties has not yet been provided.Results obtained by Multideterminantal Density Functional Theory (MD-DFT), Energy Decomposition Analysis (EDA), and Intrinsic Distortion Path (IDP) methodologies indicate how this uniqueness can be traced back tothe manganocene’s peculiar electronic structure, mainly,the degenerate ground state and close-lying electronic andspin states.This is the peer-reviewed, authors’ version of the article: Stepanović, S., Zlatar, M., Swart, M.,& Gruden, M. (2019). The Irony of Manganocene: An Interplay between the Jahn-Teller Effect and Close-Lying Electronic and Spin States. Journal of Chemical Information and Modeling, American Chemical Society (ACS), 59(5), 1806-1810. [https://doi.org/10.1021/acs.jcim.8b00870]This document is the Accepted Manuscript version of a Published Work that appeared in final form in Journal of Chemical Information and Modeling, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see [https://doi.org/10.1021/acs.jcim.8b00870]The published version: [http://cer.ihtm.bg.ac.rs/handle/123456789/2894]Supplementary data: [https://cer.ihtm.bg.ac.rs/handle/123456789/4446

    Ligand unbinding pathway and mechanism analysis assisted by machine learning and graph methods

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    We present two methods to reveal protein-ligand unbinding mechanisms in biased unbinding simulations by clustering trajectories into ensembles representing unbinding paths. The first approach is based on a contact principal component analysis for reducing the dimensionality of the input data, followed by identification of unbinding paths and training a machine learning model for trajectory clustering. The second approach clusters trajectories according to their pairwise mean Euclidean distance employing the neighbor-net algorithm, which takes into account input data bias in the distances set and is superior to dendrogram construction. Finally, we describe a more complex case where the reaction coordinate relevant for path identification is a single intra-ligand hydrogen bond, highlighting the challenges involved in unbinding path reaction coordinate detection.Comment: This preprint is the unformatted version of a manuscript that has been published as article in the Journal of Chemical Information and Modeling and can be downloaded for private use only. Copyright with ACS, the journal and the author

    Pharmacoprint -- a combination of pharmacophore fingerprint and artificial intelligence as a tool for computer-aided drug design

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    Structural fingerprints and pharmacophore modeling are methodologies that have been used for at least two decades in various fields of cheminformatics: from similarity searching to machine learning (ML). Advances in silico techniques consequently led to combining both these methodologies into a new approach known as pharmacophore fingerprint. Herein, we propose a high-resolution, pharmacophore fingerprint called Pharmacoprint that encodes the presence, types, and relationships between pharmacophore features of a molecule. Pharmacoprint was evaluated in classification experiments by using ML algorithms (logistic regression, support vector machines, linear support vector machines, and neural networks) and outperformed other popular molecular fingerprints (i.e., Estate, MACCS, PubChem, Substructure, Klekotha-Roth, CDK, Extended, and GraphOnly) and ChemAxon Pharmacophoric Features fingerprint. Pharmacoprint consisted of 39973 bits; several methods were applied for dimensionality reduction, and the best algorithm not only reduced the length of bit string but also improved the efficiency of ML tests. Further optimization allowed us to define the best parameter settings for using Pharmacoprint in discrimination tests and for maximizing statistical parameters. Finally, Pharmacoprint generated for 3D structures with defined hydrogens as input data was applied to neural networks with a supervised autoencoder for selecting the most important bits and allowed to maximize Matthews Correlation Coefficient up to 0.962. The results show the potential of Pharmacoprint as a new, perspective tool for computer-aided drug design.Comment: Journal of Chemical Information and Modeling (2021

    A bibliometric analysis of the Journal of Molecular Graphics and Modelling

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    This paper reviews the articles published in Volumes 2-24 of the Journal of Molecular Graphics and Modelling (formerly the Journal of Molecular Graphics), focusing on the changes that have occurred in the subject over the years, and on the most productive and most cited authors and institutions. The most cited papers are those describing systems or algorithms, but the proportion of these types of article is decreasing as more applications of molecular graphics and molecular modelling are reported
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