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: 4.07

Impact Factor Rankings

2015 Impact Factor Available summer 2015
2013 / 2014 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
Year

Additional details

5-year impact 4.07
Cited half-life 6.60
Immediacy index 0.80
Eigenfactor 0.02
Article influence 0.89
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

  • Journal of Chemical Information and Modeling 06/2015; 55(6):1087-1087. DOI:10.1021/acs.jcim.5b00361
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    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 the Molecular Operating Environment (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 (ρ = 0.806, R^2 = 0.649, p < 0.005).
    Journal of Chemical Information and Modeling 02/2015; 55(2):398-406.
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    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
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    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; DOI:10.1021/ci500520q
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    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; DOI:10.1021/ci500681r
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    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
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    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
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    ABSTRACT: Molecular similarity methods have played a crucial role in the success of structure-based and computer-assisted drug design. However, with the exception of CoMSIA, the current approaches for estimating molecular similarity yield a global picture thereby providing limited information about the local spatial molecular features responsible for the variation of activity with the 3D structure. Application of molecular similarity measures, each related to the functional "pieces" of a ligand-receptor complex is advantageous over a composite molecular similarity alone and will provide more insights to rationally interpret the activity based on the receptor and ligand structural features. Building on the ideas of our previously published methodologies - CoRIA and LISA, we present here a local molecular similarity based receptor dependent - QSAR method termed CoRILISA which is a hybrid of the two approaches. The method improves on previous techniques by inclusion of receptor attributes for the calculation and comparison of similarity between molecules. For validation studies, the CoRILISA methodology was applied on three large and diverse data sets- glycogen phosphorylase b (GPb), human immunodeficiency virus - 1 protease (HIV PR) and cyclin dependent kinase 2 (CDK2) inhibitors. The statistics of the CoRILISA models was benchmarked against standard CoRIA approach and with the other published approaches. The CoRILISA models were found to be significantly better, especially in terms of the predictivity for the test set. CoRILISA is able to identify the thermodynamic properties associated with residues that define the active site and modulate the variation in the activity of the molecules. It is a useful tool in the fragment-based drug discovery approach for ligand activity prediction.
    Journal of Chemical Information and Modeling 12/2014; 55(1). DOI:10.1021/ci5006367
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    ABSTRACT: As part of the CSAR 2013 benchmark exercise, we have implemented a hybrid docking and scoring workflow to rank 10 steroid ligands of an engineered digoxigenin-binding protein. Schrödinger's Glide docking software was used to generate poses for each steroid ligand and rank them according to both standard docking precision (SP) and extra docking precision (XP) scoring functions. The unique component of our approach was the use of a target-specific pose classifier trained to discriminate nativelike from decoy poses. To build the classifier, a single cognate ligand with a known native pose (PDB code 4J8T ) was docked multiple times into its target protein, and the generated poses were divided into two classes (nativelike and decoy) using a root-mean-square deviation threshold of 2 Å. All of the poses were characterized by the MCT-Tess descriptors of the protein-ligand interface, and random forest (RF) models were trained to discriminate the two classes of poses on the basis of their descriptors. The consensus pose classifier was then applied to the Glide-generated poses of each CSAR ligand in order to filter out those poses predicted as decoys and rerank the remaining ones using both XP and SP scoring functions. The best-scoring pose for each ligand following this filtering step was used for final ligand ranking. Overall, the ranking accuracy for the 10 ligands evaluated by the Spearman correlation coefficient was 0.64 for SP and 0.52 for XP but reached 0.75 for SP/RF consensus scoring (ranked third in the CSAR 2013 benchmark exercise). This study reconfirms that target-specific pose scoring models are capable of enhancing the reliability of structure-based molecular docking by discarding decoy poses.
    Journal of Chemical Information and Modeling 12/2014; 55(1). DOI:10.1021/ci500519w
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    ABSTRACT: The study of chromatographic retention of natural products can be used to increase their identification speed in complex biological matrices. In this work, six variables were used to study the retention behavior in reversed phase liquid chromatography of 39 sesquiterpene lactones (SL) from an in-house database using chemoinformatics tools. To evaluate the retention of the SL, retention parameters on an ODS C-18 Shimadzu column, in two different solvent systems were experimentally obtained, namely MeOH-H2O 55:45 and MeCN-H2O 35:75. The chemoinformatics approach involved three descriptor type sets (one 2D and two 3D) comprising three groups of each (four, five and six descriptors), two different training and test sets, four algorithms for variable selection (best-first, linear forward, greedy stepwise and genetic algorithm), and two modeling methods [partial least square regression (PLS) and back-propagation artificial neural network (ANN)]. The influence of the six variables used in this study was assessed in a holistic context, and influences on the best model for each solvent system were analyzed. The best set for MeOH-H2O showed acceptable correlation statistics with a training R(2) = 0.91, cross-validation Q(2) = 0.88, and external validation P(2) = 0.80 and the best MeCN-H2O model showed much higher correlation statistics with a training R(2) = 0.96, cross-validation Q(2) = 0.92, and external validation P(2) = 0.91. Consensus models were built for each chromatographic system and although all of them showed an improved statistical performance, only one for the MeCN-H2O system was able to separate isomers as well as to improve the performance. The approach described herein can therefore be used to generate reproducible and robust models for QSRR studies of natural products as well as an aid for dereplication of complex biological matrices using plant metabolomics based techniques.
    Journal of Chemical Information and Modeling 12/2014; 55(1). DOI:10.1021/ci500581q
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    ABSTRACT: Intelligent Automatic Design (IADE) is an expert system developed at Novartis to identify non-classical bioisosteres. In addition to bioisostere search, one could also use IADE to grow a fragment bound to a protein. Here, we report an evaluation of IADE as a tool for fragment growing. Three examples from the literature served as test case. In each case, IADE could generate close analogs of the published compounds and reproduce their crystallographic binding mode. This exercise validated the use of the IADE system for fragment growing. We have also gained experience to optimize the performance of IADE for this type of applications.
    Journal of Chemical Information and Modeling 12/2014; 55(1). DOI:10.1021/ci5006355
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    ABSTRACT: Growing data sets with increased time for analysis is hampering predictive modeling in drug discovery. Model building can be carried out on high-performance computer clusters, but these can be expensive to purchase and maintain. We have evaluated ligand-based modeling on cloud computing resources where computations are parallelized and run on Amazon Elastic Cloud. We train models on open datasets of varying sizes for the endpoints logP and Ames mutagenicity and compare with model building parallelized on a traditional high-performance computing cluster. We show that while high-performance computing results in faster model building, the use of cloud computing resources is feasible for large datasets and scales well within cloud instances. An additional advantage of cloud computing is that the costs of predictive models can be easily quantified, and a choice can be made between speed and economy. The easy access to computational resources with no up-front investments makes cloud computing an attractive alternative for scientists, especially for those without access to a supercomputer, and our study shows that it enables cost-efficient modeling of large data sets on demand within reasonable time.
    Journal of Chemical Information and Modeling 12/2014; 55(1). DOI:10.1021/ci500580y
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    ABSTRACT: Glycosaminoglycans (GAGs) represent a class of anionic periodic linear polysaccharides, which mediate cell communication processes by interactions with their protein targets in the extracellular matrix. Due to their high flexibility, charged nature, periodicity, and polymeric nature GAGs are challenging systems for computational approaches. To deal with the length challenge, coarse-grained (CG) modeling could be a promising approach. In this work, we develop AMBER compatible CG parameters for GAGs using all-atomic (AA) molecular dynamics (MD) simulations in explicit solvent and the Boltzmann conversion approach. We compare both global and local properties of GAGs obtained in the simulations with AA and CG approaches, and we conclude that our CG model is appropriate for the MD approach of long GAG molecules at long time scales.
    Journal of Chemical Information and Modeling 12/2014; 55(1). DOI:10.1021/ci500669w