449,897 research outputs found
The literature of chemoinformatics : 1978–2018
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
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
. 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 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
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
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Supporting Material for: Enthalpy of Solvation Correlations for Gaseous Solutes Dissolved in Water and in 1-Octanol Based on the Abraham Model
This document includes supporting material for an article titled, "Enthalpy of solvation correlations for gaseous solutes dissolved in water and in 1-octanol based on the Abraham model," published in the Journal of Chemical Information and Modeling
The Irony of Manganocene: An Interplay between the Jahn-Teller Effect and Close-Lying Electronic and Spin States
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
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
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
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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