Journal of Chemical Information and Modeling (J CHEM INF MODEL)

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

2016 Impact Factor Available summer 2017
2014 / 2015 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
Year

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: A framework for molecular complexity is established that is based on information theory and consistent with chemical knowledge. The resulting complexity index Cm is derived from abstracting the information content of a molecule by the degrees of freedom in the microenvironments on a per atom base that allows calculating molecular complexity in a simple and additive way. This index allows to universally assess complexities of any molecule and is sensitive to stereochemistry, heteroatoms, and symmetry. The performance of this complexity index is evaluated and compared against the current state of the art. Its additive character gives consistent values also for very large molecules and supports direct comparisons of chemical reactions. Finally, this approach may provide a useful tool for medicinal chemistry in drug design and lead selection, which is demonstrated by correlating molecular complexities of antibiotics with compound specific parameters.
    No preview · Article · Feb 2016 · Journal of Chemical Information and Modeling
  • [Show abstract] [Hide abstract]
    ABSTRACT: The increase in compounds with activity against five major therapeutic target families has been quantified on a time scale and investigated employing a compound-scaffold-cyclic skeleton (CSK) hierarchy. The analysis was designed to better understand possible reasons for target-dependent growth of bioactive compounds. There was strong correlation between compound and scaffold growth across all target families. Active compounds becoming available over time were mostly represented by new scaffolds. On the basis of scaffold-to-compound ratios, new active compounds were structurally diverse and, on the basis of CSK-to-scaffold ratios, often had previously unobserved topologies. In addition, novel targets emerged that complemented major families. The analysis revealed that compound growth is associated with increasing chemical diversity and that current pharmaceutical targets are capable of recognizing many structurally different compounds, which provides a rationale for the rapid increase in the number of bioactive compounds over the past decade. In light of these findings, it is likely that new chemical entities will be discovered for many small molecule targets including relatively unexplored ones as well as for popular and well-studied therapeutic targets. Moreover, given the wealth of new "active scaffolds" that have been increasingly identified for many targets over time, computational scaffold hopping exercises should generally have a high likelihood of success.
    No preview · Article · Feb 2016 · Journal of Chemical Information and Modeling
  • [Show abstract] [Hide abstract]
    ABSTRACT: Prediction of the three-dimensional (3D) structures of proteins by computational methods is acknowledged as an unsolved problem. Accurate prediction of important structural characteristics such as contact number is expected to accelerate the otherwise slow progress being made in the prediction of 3D structure of proteins. Here, we present a dropout neural network-based method, TMH-Expo, for predicting the contact number of transmembrane helix (TMH) residues from sequence. Neuronal dropout is a strategy where certain neurons of the network are excluded from back-propagation to prevent co-adaptation of hidden-layer neurons. By using neuronal dropout, overfitting was significantly reduced and performance was noticeably improved. For multi-spanning helical membrane proteins, TMH-Expo achieved a remarkable Pearson correlation coefficient of 0.69 between predicted and experimental values and a mean absolute error of only 1.68. In addition, among those membrane protein-membrane protein interface residues, 76.8% were correctly predicted. Mapping of predicted contact numbers onto structures indicates that contact numbers predicted by TMH-Expo reflect the exposure patterns of TMHs and reveal membrane protein-membrane protein interfaces, reinforcing the potential of predicted contact numbers to be used as restraints for 3D structure prediction and protein-protein docking. TMH-Expo can be accessed via a webserver at www.meilerlab.org.
    No preview · Article · Jan 2016 · Journal of Chemical Information and Modeling
  • [Show abstract] [Hide abstract]
    ABSTRACT: Bayesian models constructed from structure-derived fingerprints have been a popular and useful method for drug discovery research when applied to bioactivity measurements that can be effectively classified as active or inactive. The results can be used to rank candidate structures according to their probability of activity, and this ranking benefits from the high degree of interpretability when structure-based fingerprints are used, making the results chemically intuitive. Besides selecting an activity threshold, building a Bayesian model is fast and requires few or no parameters or user intervention. The method also does not suffer from such acute overtraining problems as quantitative structure activity relationships or quantitative structure property relationships (QSAR/QSPR). This makes it an approach highly suitable for automated workflows that are independent of user expertise or prior knowledge of the training data. We now describe a new method for creating a composite group of Bayesian models to extend the method to work with multiple states, rather than just binary. Incoming activities are divided into bins, each covering a mutually exclusive range of activities. For each of these bins, a Bayesian model is created to model whether or not the compound belongs in the bin. Analyzing putative molecules using the composite model involves making a prediction for each bin, and examining the relative likelihood for each assignment, e.g. highest value wins. The method has been evaluated on a collection of hundreds of datasets extracted from ChEMBL v20 and validated datasets for ADME/Tox and bioactivity.
    No preview · Article · Jan 2016 · Journal of Chemical Information and Modeling
  • [Show abstract] [Hide abstract]
    ABSTRACT: Quantitative structure-activity relationships (QSAR) modelling has matured over the past 50 years and has been very useful in discovering and optimising drug leads. Although its roots were in extra-thermodynamic relationships within small sets of chemically similar molecules focused on mechanistic interpretation, a second class of QSAR models has emerged that relies on statistical or machine learning methods to generate models from large, chemically diverse data sets for predictive purposes. There has been a tension between the two groups of QSAR practitioners that is unnecessary and possibly counterproductive. This paper explains the difference in philosophy and application of these two distinct classes of QSAR models, and how they can work together synergistically to accelerate the discovery of new drugs or materials.
    No preview · Article · Jan 2016 · Journal of Chemical Information and Modeling
  • [Show abstract] [Hide abstract]
    ABSTRACT: Increased reports of oseltamivir (OTV) resistant strains of the influenza virus, such as H274Y mutation on its neuraminidase (NA), have created some cause for concern. Many studies were conducted in the attempt to uncover the mechanism of OTV resistance in H274Y NA. However, most of the reported studies on H274Y only focused on the drug-bound system, but direct effects of the mutation toward NA itself prior to drug binding still remain unclear. Therefore, molecular dynamics simulations of NA in apo form, followed by principal component analysis and interaction energy calculation, were performed to investigate the structural changes of the NA binding site as a result of H274Y mutation. It was observed that the disruption of the NA binding site due to H274Y was initiated by the repulsive effect of Y274 on the 250-loop, which in turn altered the hydrogen bond network around residue 274. The rotated W295 side chain caused the upward movement of the 340-loop. Consequently, sliding box docking results suggested that the binding pathway of OTV was compromised due to the disruption of this binding site. This study also highlighted the importance of the functional group at position C6 of sialic acid mimicry. It is hoped that these results could improve the understanding of OTV resistances and shed some light on the design of a novel anti-influenza drug.
    No preview · Article · Dec 2015 · Journal of Chemical Information and Modeling
  • [Show abstract] [Hide abstract]
    ABSTRACT: The binding of a small molecule ligand to its protein target is most often characterized by binding affinity and is typically viewed as an on/off switch. The more complex reality is that binding involves the ligand passing through a series of intermediate states between the solution phase and the fully bound pose. We have performed a set of 29 unbiased molecular dynamics simulations to model the binding pathways of the dopamine receptor antagonists clozapine and haloperidol binding to the D2 and D3 dopamine receptors. Through these simulations we have captured the binding pathways of clozapine and haloperidol from the extracellular vestibule to the orthosteric binding site and thereby, we also predict the bound pose of each ligand. These are the first long timescale simulations of haloperidol or clozapine binding to dopamine receptors. From these simulations, we have identified several important stages in the binding pathway, including the involvement of Tyr7.35 in a 'handover' mechanism that transfers the ligand between the extracellular vestibule and Asp3.32. We have also performed interaction and cluster analyses to determine differences in binding pathways between the D2 and D3 receptors and identified metastable states that may be of use in drug design.
    No preview · Article · Dec 2015 · Journal of Chemical Information and Modeling
  • [Show abstract] [Hide abstract]
    ABSTRACT: The development of novel anti-influenza drugs is of great importance because of the capability of influenza viruses to occasionally cross inter-species barriers and to rapidly mutate. One class of anti-influenza agents, aminoadamantanes, including the drugs amantadine and rimantadine now widely abandoned due to virus resistance, bind to and block the pore of the transmembrane domain of the M2 proton channel (M2TM) of influenza A. Here, we present one of the still rare studies that interprets thermodynamic profiles from isothermal titration calorimetry (ITC) experiments in terms of individual energy contributions to binding, calculated by the computationally inexpensive implicit solvent / implicit membrane molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) approach, for aminoadamantane compounds binding to M2TM of the avian "Weybridge" strain. For all eight pairs of aminoadamantane compounds considered, the trend of the predicted relative binding free energies and their individual components, effective binding energies and changes in the configurational entropy, agrees with experimental measures (DDG, DDH, TDDS) in 88, 100, and 50% of the cases. In addition, information yielded by the MM-PBSA approach about determinants of binding goes beyond that available in component quantities (DH, DS) from ITC measurements. We demonstrate how one can make use of such information to link thermodynamic profiles from ITC with structural causes on the ligand side and, ultimately, to guide decision making in lead optimization in a prospective manner, which results in an aminoadamantane derivative with improved binding affinity against M2TMWeybridge.
    No preview · Article · Dec 2015 · Journal of Chemical Information and Modeling
  • [Show abstract] [Hide abstract]
    ABSTRACT: Drug binding involves changes of the local water structure around proteins, also considering water rearrangements across surface-solvation layers around protein and ligand portions exposed to the newly formed complex surface. For a series of thermolysin-binding phosphonamidates we discovered that variations of the partly exposed P2'-substituents modulate binding affinity up to 10 kJ mol-1 with even larger enthalpy/entropy partitioning of the binding signature. The observed profiles cannot be explained by mere desolvation effects, instead quality and completeness of the formed surface-water network wrapping around the formed complexes provide an explanation for the discovered structure-activity relationship. We used molecular dynamics to compute surface water networks and predict solvation sites around the complexes. Qualitatively, a fairly good correlation with experimental difference electron densities in high-resolution crystal structures is achieved, in detail problems with the potentials are discovered: Charge-assisted contacts to waters are exaggerated by AMBER and stabilizing contributions of water-to-methyl contacts are underestimated.
    No preview · Article · Dec 2015 · Journal of Chemical Information and Modeling
  • [Show abstract] [Hide abstract]
    ABSTRACT: Structurally conserved water molecules are important for biomolecular stability, flexibility and function. X-ray crystallographic studies of Pin1 have resolved a number of water molecules around the enzyme, including two highly conserved water molecules within the protein. The functional role of these localized water molecules remains unknown and unexplored. Pin1 catalyzes cis/trans isomerizations of peptidyl prolyl bonds that are preceded by a phosphorylated serine or threonine residue. Pin1 is involved in many subcellular signaling processes and is a potential therapeutic target for the treatment of several life threatening diseases. Here, we investigate the significance of these structurally conserved water molecules in the catalytic domain of Pin1 using molecular dynamics (MD) simulations, free energy calculations, analysis of X-ray crystal structures, and circular dichroism (CD) experiments. MD simulations and free energy calculations suggest the tighter binding water molecule plays a crucial role in maintaining the integrity and stability of a critical hydrogen-bonding network in the active site. The second water molecule is exchangeable with bulk solvent and is found in a distinctive helix-turn-coil motif. Structural bioinformatics analysis of non-redundant X-ray crystallographic protein structures in the Protein Data Bank (PDB) suggest this motif is present in several other proteins and can act as a water site, akin to the calcium EF hand. CD experiments suggest the isolated motif is in a distorted PII conformation and requires the protein environment to fully form the α-helix-turn-coil motif. This study provides valuable insights into the role of hydration in the structural integrity of Pin1 that can be exploited in protein engineering and drug design.
    No preview · Article · Dec 2015 · Journal of Chemical Information and Modeling
  • [Show abstract] [Hide abstract]
    ABSTRACT: The intrinsic relationships between nanoscale features and electronic properties of nanomaterials remains poorly investigated. In this work, electronic properties of 622 computationally optimized graphene structures are mapped to their structures using partial-least square regression and radial distributions function (RDF) scores. Quantitative Structure-Property Relationship (QSPR) were calibrated with 70% of a virtual dataset of 622 passivated and non-passivated graphenes and we predict the properties of the remaining 30% of the structures. The analysis of the optimum QSPR models reveals that the most relevant RDF scores appear at interatomic distance in the range of 2.0 Å to 10.0 Å for the energy of the Fermi level and the electron affinity, whilst the electronic band gap and the ionization potential correlates to RDF scores in a wider range from 3.0 Å to 30.0 Å. The predictions were more accurate for the energy of the Fermi level and the ionization potential with more than 83% of explained data variance, whilst the electron affinity exhibits a value of ∽80% and the energy of the band gap a lower 70%. QSPR models have tremendous potential to rapidly identify hypothetical nanomaterials with desired electronic properties that could be experimentally prepared in the near future.
    No preview · Article · Nov 2015 · Journal of Chemical Information and Modeling
  • [Show abstract] [Hide abstract]
    ABSTRACT: In this paper we present a new method for finding the optimal path to pull ligand from the binding pocket using steered molecular dynamics (SMD). Scoring function is defined as the steric hindrance caused by receptor to ligand movement. Then the optimal path corresponds to minimum of this scoring function. We call the new method MSH (Minimal Steric Hindrance). Contrary to existing navigation methods, our approach takes into account geometry of ligand while other methods including CAVER only consider ligand as sphere with a given radius. Using three different target+receptor sets, we have shown that the rupture force F_max and non-equilibrium work W_pull obtained based on MSH method show much higher correlation with experimental data on binding free energy compared to CAVER. Furthermore, W_pull was found to be a better indicator for binding affinity than F_max. Thus, new MSH method is a reliable tool for obtaining the best direction for ligand exciting from the binding site. Its combination with the standard SMD technique can provide reasonable results for ranking binding affinities using W_pull as a scoring function.
    No preview · Article · Nov 2015 · Journal of Chemical Information and Modeling