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
    ​ white

Publications in this journal

  • [Show abstract] [Hide abstract]
    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
  • [Show abstract] [Hide abstract]
    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; DOI:10.1021/acs.jcim.5b00262
  • [Show abstract] [Hide abstract]
    ABSTRACT: Drug resistance of mutations V32I, G48V, I50V, I54V and I84V in HIV-1 protease (PR) were found in clinical treatment of HIV patients with the drug Amprenavir (APV). In order to elucidate the molecular mechanism of drug resistance associated with these mutations, thermodynamic integration (TI) and molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) methods were applied to calculate binding free energies of APV to these mutated and wild type PRs. The relative binding free energy difference from TI calculation reveals that the decrease in van der Waals interaction of APV with mutated PRs relative to the wild-type PR mainly drives the drug resistance. This result is in good agreement with the previous experimental results, and is also consistent with the result from MM-PBSA calculations. Analyses based on molecular dynamics trajectory show that these mutations can adjust the shape and conformation of the binding pocket, which provides main contributions to the decrease in the van der Waals interactions of APV with mutated PRs. The present study could provide important guidance for designing new potent inhibitors that could alleviate drug resistance of PR due to mutations.
    Journal of Chemical Information and Modeling 08/2015; DOI:10.1021/acs.jcim.5b00173
  • [Show abstract] [Hide abstract]
    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; DOI:10.1021/acs.jcim.5b00319
  • Journal of Chemical Information and Modeling 06/2015; 55(6):1087-1087. DOI:10.1021/acs.jcim.5b00361
  • [Show abstract] [Hide abstract]
    ABSTRACT: During drug development compounds are tested against counterscreens, a panel of off-target activities that would be undesirable for a drug to have. Testing every compound against every counterscreen is generally too costly in terms of time and money and we need to find a rational way of prioritizing counterscreen testing. Here we present the eCounterscreening paradigm, wherein predictions from QSAR models for counterscreen activity are used to generate a recommendation as to whether a specific compound in a specific project should be tested against a specific counterscreen. The rules behind the recommendations, which can be summarized in a risk-benefit plot specific for a project/counterscreen combination, are based on a previously assembled database of prospective QSAR predictions. The recommendations require two user-defined cutoffs: what level of activity in a specific counterscreen is considered undesirable, and what level of risk the chemist is willing to accept that an undesired counterscreen activity will go undetected. We demonstrate in a simulated prospective experiment that eCounterscreening can be used to postpone a large fraction of counterscreen testing and still have an acceptably low risk of undetected counterscreen activity.
    Journal of Chemical Information and Modeling 12/2014; 55(2). DOI:10.1021/ci500666m
  • [Show abstract] [Hide abstract]
    ABSTRACT: Maltose-binding protein is a periplasmic binding protein responsible for transport of maltooligosaccarides through the periplasmic space of gram negative bacteria, as a part of the ABC transport system. The molecular mechanisms of the initial ligand binding and induced large scale motion of the protein's domains still remain elusive. In this study we use a new docking protocol which combines a recently proposed explicit water placement algorithm based on the 3D-RISM-KH molecular theory of solvation and conventional docking software (AutoDock Vina) to explain the mechanisms of maltotriose binding to the apo-open state of maltose-binding protein. We confirm the predictions of previous NMR spectroscopic experiments on binding modes of the ligand. We provide the molecular details on the binding mode which was not previously observed in the X-ray experiments. We show that this mode which is defined by the fine balance between the protein-ligand direct interactions and solvation effects, can trigger the protein's domain motion resulting in the holo-closed structure of the maltotriose-maltose-binding protein in excellent agreement with the experimental data. We also discuss a role of water in blocking unfavorable binding sites and water-mediated interactions contributing to stability of observable binding modes of maltotriose.
    Journal of Chemical Information and Modeling 12/2014; 55(2). DOI:10.1021/ci500520q
  • [Show abstract] [Hide abstract]
    ABSTRACT: The formation of a covalent bond with the target is essential for a number of successful drugs, yet tools for covalent docking without significant restrictions regarding warhead or receptor classes are rare and limited in use. In this work we present DOCKTITE, a highly versatile workflow for covalent docking in MOE combining automated warhead screening, nucleophilic side chain attachment, pharmacophore-based docking and a novel consensus scoring approach. The comprehensive validation study includes pose predictions of 35 protein/ligand complexes which resulted in a mean RMSD of 1.74 Å and a prediction rate of 71.4% with an RMSD below 2 Å, a virtual screening with an area under the curve (AUC) for the Receiver Operating Characteristics (ROC) of 0.81 and a significant correlation between predicted and experimental binding affinities (rho = 0.806, R² = 0.649, p < 0.005).
    Journal of Chemical Information and Modeling 12/2014; 55(2). DOI:10.1021/ci500681r
  • [Show abstract] [Hide abstract]
    ABSTRACT: Fingerprint methods applied to molecules have proven to be useful for similarity determination and as inputs to machine-learning models. Here we present the development of a new fingerprint for chemical reactions and validate its usefulness in building machine-learning models and in similarity assessment. Our final fingerprint is constructed as the difference of the atom-pair fingerprints of products and reactants and includes agents via calculated physico-chemical properties. We validated the fingerprints on a large data set of reactions text-mined from granted US patents from the last 40 years which have been classified using a substructure-based expert system. We applied machine learning to build a 50-class predictive model for reaction-type classification which correctly predicts 97% of the reactions in an external test set. Impressive accuracies were also observed when applying the classifier to reactions from an in-house electronic laboratory notebook. The performance of the novel fingerprint for assessing reaction similarity was evaluated by a cluster analysis which recovered 48 out of 50 of the reaction classes with a median F-score of 0.63 for the clusters. The data sets used for training and primary validation as well as all python scripts required to reproduce the analysis are provided in the Supplementary Information.
    Journal of Chemical Information and Modeling 12/2014; DOI:10.1021/ci5006614
  • [Show abstract] [Hide abstract]
    ABSTRACT: The ability of the insulin-degrading enzyme (IDE) to degrade amyloid-β 42 (Aβ42), a process regulated by ATP, has been studied as an alternative path in the development of drugs against Alzheimer's disease. In this study, we calculated the potential of mean force for the degradation of Aβ42 by IDE in the presence and absence of ATP by umbrella sampling with hybrid quantum mechanics and molecular mechanics (QM/MM) calculations, using the SCC-DFTB QM Hamiltonian and Amber ff99SB force field. Results indicate that the reaction occurs in two steps: the first step is characterized by the formation of the intermediate and the second by breaking the peptide bond of the substrate, the latter being the rate determining step. In our simulations, the activation energy barrier in the absence of ATP is 15 ± 2 kcal·mol-1, which is 7 kcal·mol-1 lower than in the presence of ATP, indicating that the presence of the nucleotide decreases the reaction rate by a about 105 times.
    Journal of Chemical Information and Modeling 12/2014; 55(1). DOI:10.1021/ci500544c