Journal of Chemical Information and Modeling Impact Factor & Information

Publisher: American Chemical Society, American Chemical Society

Journal description

Papers reporting new methodology or important applications in the fields of chemical informatics or molecular modeling are appropriate for submission to this Journal. Specific topics include: representation and computer-based searching of chemical databases; computer-aided molecular design; development of new computational methods or efficient algorithms for chemical software; biopharmaceutical chemistry including analyses of biological activity and other issues; related to drug discovery.

Current impact factor: 3.74

Impact Factor Rankings

2015 Impact Factor Available summer 2016
2014 Impact Factor 3.738
2013 Impact Factor 4.068
2012 Impact Factor 4.304
2011 Impact Factor 4.675
2010 Impact Factor 3.822
2009 Impact Factor 3.882
2008 Impact Factor 3.643
2007 Impact Factor 2.986
2006 Impact Factor 3.423
2005 Impact Factor 2.923

Impact factor over time

Impact factor

Additional details

5-year impact 3.92
Cited half-life 6.40
Immediacy index 0.72
Eigenfactor 0.02
Article influence 0.91
Website Journal of Chemical Information and Modeling website
Other titles Journal of chemical information and modeling (Online), Journal of chemical information and modeling
ISSN 1549-9596
OCLC 54952610
Material type Document, Periodical, Internet resource
Document type Internet Resource, Computer File, Journal / Magazine / Newspaper

Publisher details

American Chemical Society

  • Pre-print
    • Author cannot archive a pre-print version
  • Restrictions
    • Must obtain written permission from Editor
    • Must not violate ACS ethical Guidelines
  • Post-print
    • Author cannot archive a post-print version
  • Restrictions
    • If mandated by funding agency or employer/ institution
    • If mandated to deposit before 12 months, must obtain waiver from Institution/Funding agency or use AuthorChoice
    • 12 months embargo
  • Conditions
    • On author's personal website, pre-print servers, institutional website, institutional repositories or subject repositories
    • Non-Commercial
    • Must be accompanied by set statement (see policy)
    • Must link to publisher version
    • Publisher's version/PDF cannot be used
    • If mandated sooner than 12 months, must obtain waiver from Editors or use AuthorChoice
    • Reviewed on 07/08/2014
  • Classification

Publications in this journal

  • Journal of Chemical Information and Modeling 11/2015; DOI:10.1021/acs.jcim.5b00386
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    ABSTRACT: Graph data are becoming increasingly common in machine learning and data mining, and its application field pervades to bioinformatics and cheminformatics. Accordingly, methods to extract patterns from graph data; graph mining has been studied and developed rapidly these years. Since the number of patterns in graph data is huge, a central issue is how to efficiently collect informative patterns suitable for subsequent tasks such as classification or regression. In this paper we consider mining discriminative subgraphs from graph data with multiple labels. The resulting task has important applications in cheminformatics, such as finding com- mon functional groups that trigger multiple drug side effects, or identifying ligand functional groups that hit multiple targets. In computational experiments, we first verify the effectiveness of the proposed approach in synthetic data, then we apply it to drug adverse effect prediction problem. In the latter dataset, we compared the proposed method with L1-norm logistic regression in combination with Pub- Chem/Open Babel fingerprint, in that the proposed method showed superior performance with much less number of subgraph patterns.
    Journal of Chemical Information and Modeling 11/2015; DOI:10.1021/acs.jcim.5b00376
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    ABSTRACT: Recent advances in interaction design have created new ways to use computers. One example is the ability to create enhanced 3D environments that simulate physical presence in the real world-a virtual reality. This is relevant to drug discovery since molecular models are frequently used to obtain deeper understandings of, say, ligand-protein complexes. We have developed a tool (Molecular Rift), which creates a virtual reality environment steered with hand movements. Oculus Rift, a head-mounted display, is used to create the virtual settings. The program is controlled by gesture-recognition, using the gaming sensor MS Kinect v2, eliminating the need for standard input devices. The Open Babel toolkit was integrated to provide access to powerful cheminformatics functions. Molecular Rift was developed with a focus on usability, including iterative test-group evaluations. We conclude with reflections on virtual reality's future capabilities in chemistry and education. Molecular Rift is open source and can be downloaded from GitHub.
    Journal of Chemical Information and Modeling 11/2015; DOI:10.1021/acs.jcim.5b00544
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    ABSTRACT: The 2014 CSAR Benchmark Exercise was focused on three protein targets: Coagulation Factor Xa (FXA), Spleen Tyrosine Kinase (SYK) and bacterial tRNA methyltransferase (TRMD). Our protocol involved a preliminary analysis of the structural information available in the PDB for the protein targets which allowed the identification of the most appropriate docking software and scoring functions to be used for the rescoring of several docking conformations datasets, as well as for pose prediction and affinity ranking. The two key points of this study were the prior evaluation of molecular modeling tools that are most adapted for each target and the increased search efficiency during the docking process to better explore the conformational space of big and flexible ligands.
    Journal of Chemical Information and Modeling 09/2015; DOI:10.1021/acs.jcim.5b00337
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    ABSTRACT: Pcetk (a pDynamo-based continuum electrostatic toolkit) is an open-source, object-oriented toolkit for the calculation of proton binding energetics in proteins. The toolkit is a module of the pDynamo software library, combining the versatility of the Python scripting language and the efficiency of the compiled languages, C and Cython. In the toolkit, we have connected pDynamo to the external Poisson-Boltzmann solver, extended-MEAD. Our goal was to provide a modern and extensible environment for the calculation of protonation states, electrostatic energies, titration curves and other electrostatic dependent properties of proteins. Pcetk is freely available under the CeCILL license, which is compatible with the GNU General Public License. The toolkit can be found on the Web at the address The calculation of protonation states in proteins requires a knowledge of pKa values of protonatable groups in aqueous solution. However, for some groups, such as protonatable ligands bound to protein, the aqueous pKa values are often difficult to obtain from experiment. As a complement to Pcetk, we describe a computational method for the estimation of aqueous pKa values that has an accuracy of +/- 0.5 pKa-units or better. Finally, we verify the Pcetk module and the method for estimating aqueous pKa values with different model cases.
    Journal of Chemical Information and Modeling 09/2015; 55(10):150922012829001. DOI:10.1021/acs.jcim.5b00262
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    ABSTRACT: Assessment of accurate drug binding affinity to a protein remains a challenge for in silico drug development. In this research, we used the smooth reaction path generation (SRPG) method to calculate binding free energies and determine potential of mean forces (PMFs) along the smoothed dissociation paths of influenza A neuraminidase and its variants with oseltamivir (Tamiflu) and zanamivir (Relenza) inhibitors. With the gained results, we found that the binding free energies of neuraminidase A/H5N1 in WT and two mutants (including H274Y and N294S) with oseltamivir and zanamivir show good agreement with experimental results. Additionally, the thermodynamic origin of the drug resistance of the mutants was also discussed from the PMF profiles.
    Journal of Chemical Information and Modeling 08/2015; 55(9). DOI:10.1021/acs.jcim.5b00319
  • Source

    Journal of Chemical Information and Modeling 06/2015; 55(6):1087-1087. DOI:10.1021/acs.jcim.5b00361