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

Publisher: American Chemical Society, American Chemical Society

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.

  • Impact factor
    4.30
    Show impact factor history
     
    Impact factor
  • 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
    • Must obtain written permission from Editor confirming posting does not conflict prior publication policies
    • If mandated to deposit before 12 months, must obtain waiver from Institution/ Agency or use AuthorChoice
    • 12 months
  • Conditions
    • On website or repositories
    • Non-Commercial
    • Must be accompanied by set statement (see policy)
    • Must link to publisher version
    • If mandated sooner than 12 months, must obtain waiver from Editors or use AuthorChoice
    • Publisher's version/PDF may be used, but only via AuthorChoice option
  • Classification
    ​ white

Publications in this journal

  • [Show abstract] [Hide abstract]
    ABSTRACT: Aroma and flavour are important factors of fruit quality and consumer preference. The specific pattern of aroma is generated during ripening by the accumulation of volatiles compounds, which are mainly esters. Alcohol acyltransferase (AAT) (EC 2.3.1.84) catalyzes the esterification reaction of aliphatic and aromatic alcohols and acyl-CoA into esters in fruits and flowers. In Fragaria x ananassa, there are different volatiles compounds that are obtained from different alcohol precursors, where octanol and hexanol are the most abundant during fruit ripening. At present, there is not structural evidence about the mechanism used by the AAT to synthesize esters. Experimental data attribute the kinetic role of this enzyme to 2 aminoacidic residues in a highly conserved motif (HXXXD) that is located in the middle of the protein. With the aim to understand the molecular and energetic aspects of volatiles compound production from F. x ananassa, we first studied the binding modes of a series of alcohols, and also different acyl-CoA substrates, in a molecular model of alcohol acyltransferase from Fragaria ananassa (FaAAT) using molecular docking. Afterwards, the dynamical behavior of both substrates, docked within the FaAAT binding site, was studied using molecular dynamics (MD) simulations. In addition, in order to correlate the experimental and theoretical data obtained in our laboratories, binding free energy calculations were performed; which previous results suggested that octanol presented the best affinity for FaAAT, followed by hexanol. Finally, and concerning the FaAAT molecular reaction mechanism, it is suggested from molecular dynamics simulations that the reaction mechanism goes through the formation of a ternary complex, in where the Histidine residue at the HXXXD motif deprotonates the alcohol substrates. Then, a nucleophilic attack occurs from alcohol charged oxygen atom to the carbon atom at carbonile group of the acyl CoA. This mechanism is in agreement with previuos results, obtained in our group, in alcohol acyltransferase from Vasconcellea pubescens (VpAAT1).
    Journal of Chemical Information and Modeling 10/2014; In preparation.
  • [Show abstract] [Hide abstract]
    ABSTRACT: Three anhydrous methylxanthines: caffeine (1,3,7-trimethylxanthine; 1,3,7-trimethyl-1H-purine-2,6-(3H,7H)-dione) and its two metabolites theophylline (1,3-dimethylxanthine; 1,3-dimethyl-7H-purine-2,6-dione) and theobromine (3,7-dimethyl-xanthine; 3,7-dimethyl-7H-purine-2,6-dione), which reveal multifaceted therapeutic potential, have been studied experimentally in solid state by (1)H-(14)N NMR-NQR (nuclear magnetic resonance-nuclear quadrupole resonance) double resonance (NQDR). For each compound the complete NQR spectrum consisting of 12 lines was recorded. The multiplicity of NQR lines indicates the presence of a stable β form of anhydrous caffeine at 233 K and stable form II of anhydrous theobromine at 213 K. The assignment of signals detected in NQR experiment to particular nitrogen atoms was made on the basis of quantum chemistry calculations performed for monomer, cluster, and solid at the DFT/GGA/BLYP/DPD level. The shifts due to crystal packing interactions were evaluated, and the multiplets detected by NQR were assigned to N(9) in theobromine and N(1) and N(9) in caffeine. The ordering theobromine > theophylline > caffeine site and theophylline < theobromine < caffeine according to increasing electric field gradient (EFG) at the N(1) and N(7) sites, respectively, reflects the changes in biological activity profile of compounds from the methylxanthines series (different pharmacological effects). This difference is elucidated on the basis of the ability to form intra- and intermolecular interactions (hydrogen bonds and π···π stacking interactions). The introduction of methyl groups to xanthine restricts the ability of nitrogen atoms to participate in strong hydrogen bonds; as a result, the dominating effect shifts from hydrogen bond (theobromine) to π···π stacking (caffeine). Substantial differences in the intermolecular interactions in stable forms of methylxanthines differing in methylation (site or number) were analyzed within the Hirshfeld surface-based approach. The analysis of local environment of the nitrogen nucleus permitted drawing some conclusions on the nature of the interactions required for effective processes of recognition and binding of a given methylxanthine to A1-A2A receptor (target for caffeine in the brain). Although the interactions responsible for linking neighboring methylxanthines molecules in crystals and methylxanthines with targets in the human organism can differ significantly, the knowledge of the topology of interactions provides reliable preliminary information about the nature of this binding.
    Journal of Chemical Information and Modeling 09/2014;
  • [Show abstract] [Hide abstract]
    ABSTRACT: Previous Article Next Article Just Accepted Manuscripts Computational Approaches Elucidate the Allosteric Mechanism of Human Aromatase Inhibition: A Novel Possible Route to Small-Molecule Regulation of CYP450s Activities? PDF [2430 KB]PDF w/ Links[1428 KB]Abstract Add to ACS ChemWorx Jacopo Sgrignani , Marta Bon , Giorgio Colombo , and Alessandra Magistrato J. Chem. Inf. Model., Just Accepted Manuscript DOI: 10.1021/ci500425y Publication Date (Web): September 1, 2014 Copyright © 2014 American Chemical Society Abstract Human aromatase (HA) is a P450 cytochrome (CYP) with an essential role in estrogen biosynthesis. Since more than 70% of breast cancers are positive for estrogenic receptor (ER), the reduction of estrogen physiological concentrations through HA inhibition is one of most important therapeutic strategies against this cancer type. Recently, experimental evidence showed that selected taxoxifen metabolites, which are typically used as estrogen receptor modulators (SERMs), inhibit HA through an allosteric mechanism. In this work, we present a computational protocol to: (i) characterize the structural framework, and (ii) define the atomistic details of the determinants for the non-competitive inhibition mechanism. Our calculations identify two putative binding sites able to efficiently bind all tamoxifen metabolites. Analysis of long-scale molecular dynamics simulations reveal that endoxifen, the most effective non-competitive inhibitor, induces significant enzyme rigidity by binding in one of the possible peripheral sites. The consequence of this binding event is the suppression of one of the functional enzymatic collective motions associated to breathing of substrate access channel. Moreover, an internal dynamics-based alignment of HA with six other human cytochromes shows that this collective motion is common to other members of CYP450 protein family. On this basis, our findings may thus be of help for the development of new (pan)inhibitors for the therapeutic treatment of cancer, targeting and modulating the activity of HA and other CYP enzymes and might support the development new general drug design strategies for chemoprevention and chemoprotection.
    Journal of Chemical Information and Modeling 09/2014;
  • [Show abstract] [Hide abstract]
    ABSTRACT: We describe the automated generation of libraries for predicting the geometric preferences of druglike molecules. The libraries contain distributions of molecular dimensions based on crystal structures in the Cambridge Structural Database (CSD). Searching of the libraries is performed in cascade fashion to identify the most relevant distributions in cases where precise structural features are poorly represented by existing crystal structures. The libraries are fully comprehensive for bond lengths, valence angles and rotamers, and produce templates for the large majority of unfused and fused rings. Geometry distributions for rotamers and rings take into account any atom chirality that may be present. Library validation has been performed on a set of druglike molecules whose structures were published after the latest CSD entry contributing to the libraries. Hence, the validation gives a true indication of prediction accuracy.
    Journal of Chemical Information and Modeling 08/2014;
  • [Show abstract] [Hide abstract]
    ABSTRACT: The accurate prediction of the adsorption energies of unsaturated molecules on graphene in the presence of water is essential for the design of molecules which can modify its properties, and which can aid its processability. We here show that a semiempirical MO method corrected for dispersive interactions (PM6-DH2) can predict the adsorption energies of unsaturated hydrocarbons, and the effect of substitution on these values, to an accuracy comparable to DFT values, and in good agreement with experiment. The adsorption energies of TCNE, TCNQ and a number of sulfonated pyrenes are also predicted, along with the effect of hydration using the COSMO model.
    Journal of Chemical Information and Modeling 08/2014;
  • [Show abstract] [Hide abstract]
    ABSTRACT: Optical chemical structure recognition is the problem of converting a bitmap image containing a chemical structure formula into a standard structured representation of the molecule. We introduce a novel approach to this problem based on the pipelined integration of pattern recognition techniques with probabilistic knowledge representation and reasoning. Basic entities and relations (such as textual elements, points, lines, etc.) are first extracted by a low-level processing module. A probabilistic reasoning engine based on Markov logic, embodying chemical and graphical knowledge, is subsequently used to refine these pieces of information. An annotated connection table of atoms and bonds is finally assembled and converted into a standard chemical exchange format. We report a successful evaluation on two large image datasets, showing that the method compares favorably with the current state-of-the-art, especially on degraded low-resolution images. The system is available as a web server at http://mlocsr.dinfo.unifi.it.
    Journal of Chemical Information and Modeling 07/2014;
  • [Show abstract] [Hide abstract]
    ABSTRACT: Probe mapping is a common approach for identifying potential binding sites in structure-based drug design; however, it typically relies on energy minimizations of probes in the gas phase and a static protein structure. The mixed-solvent molecular dynamics (MixMD) approach was recently developed to account for full protein flexibility and solvation effects in hot-spot mapping. Our first study used only acetonitrile as a probe, and here, we have augmented the set of functional group probes through careful testing and parameter validation. A diverse range of probes are needed in order to map complex binding interactions. A small variation in probe parameters can adversely effect mixed-solvent behavior, which we highlight with isopropanol. We tested 11 solvents to identify six with appropriate behavior in TIP3P water to use as organic probes in the MixMD method. In addition to acetonitrile and isopropanol, we have identified acetone, N-methylacetamide, imidazole, and pyrimidine. These probe solvents will enable MixMD studies to recover hydrogen-bonding sites, hydrophobic pockets, protein-protein interactions, and aromatic hotspots. Also, we show that ternary-solvent systems can be incorporated within a single simulation. Importantly, these binary and ternary solvents do not require artificial repulsion terms like other methods. Within merely 5 ns, layered solvent boxes become evenly mixed for soluble probes. We used radial distributions functions to evaluate solvent behavior, determine adequate mixing, and confirm the absence of phase separation. We recommend that radial distribution functions should be used to assess adequate sampling in all mixed-solvent techniques, rather than the current practice of examining the solvent ratios at the edges of the solvent box.
    Journal of Chemical Information and Modeling 07/2014;
  • [Show abstract] [Hide abstract]
    ABSTRACT: The recent increase in the number of atomic-resolution structures of G protein-coupled receptors (GPCRs) has contributed to a deeper understanding of ligand binding to several important drug targets. However, reliable modeling of GPCR-ligand complexes for the vast majority of receptors with unknown structure remains to be one of the most challenging goals for computer-aided drug design. The GPCR Dock 2013 assessment, in which researchers were challenged to predict the crystallographic structures of serotonin 5-HT1B and 5-HT2B receptors bound to ergotamine, provided an excellent opportunity to benchmark the current state of this field. Our contributions to GPCR Dock 2013 accurately predicted the binding mode of ergotamine with RMSDs below 1.8 Å for both receptors, which included the best submissions for the 5-HT1B complex. Our models also had the most accurate description of the binding sites and receptor-ligand contacts. These results were obtained using a ligand-guided homology modeling approach, which combines extensive molecular docking screening with incorporation of information from multiple crystal structures and experimentally derived restraints. In this work, we retrospectively analyzed thousands of structures that were generated during the assessment to evaluate our modeling strategies. Major contributors to accuracy were found to be improved modeling of extracellular loop two in combination with the use of molecular docking to optimize the binding site for ligand recognition. Our results suggest that modeling of GPCR-drug complexes has reached a level of accuracy at which structure-based drug design could be applied to a large number of pharmaceutically relevant targets.
    Journal of Chemical Information and Modeling 07/2014;
  • [Show abstract] [Hide abstract]
    ABSTRACT: Physicochemical properties of compounds have been instrumental in selecting lead compounds with increased drug-likeness. However, the relationship between physicochemical properties of constituent drugs and the tendency to exhibit drug interaction has not been systematically studied. We assembled physicochemical descriptors for a set of anti-fungal compounds ('drugs') previously examined for interaction. Analyzing the relationship between molecular weight, lipophilicity, H-bond donor and H-bond acceptor values for drugs and their propensity to show pairwise antifungal drug synergy, we found that combinations of two lipophilic drugs had a greater tendency to show drug synergy. We developed a more refined decision tree model that successfully predicted drug synergy in stringent cross-validation tests based on only lipophilicity of drugs. Our predictions achieved a precision of 63%, and allowed successful prediction for 58% of synergistic drug pairs, suggesting that this phenomenon can extend our understanding for a substantial fraction of synergistic drug interactions. We also generated and analyzed a large-scale synergistic human toxicity network, in which we observed that combinations of lipophilic compounds show a tendency for increased toxicity. Thus, lipophilicity, a simple and easily-determined molecular descriptor, is a powerful predictor of drug synergy. It is well established that lipophilic compounds (i) are promiscuous; having many targets in the cell and (ii) often penetrate into the cell via the cellular membrane by passive diffusion. We discuss the positive relationship between drug lipophilicity and drug synergy in the context of potential drug synergy mechanisms.
    Journal of Chemical Information and Modeling 07/2014;
  • [Show abstract] [Hide abstract]
    ABSTRACT: Flavonoids, the vastest class of natural polyphenols, are extensively investigated for their multiple benefits on human health. Due to their physicochemical or biological properties, many representatives are considered to exhibit low selectivity among various protein targets or to plague high-throughput screening (HTS) outcomes. The aim of this study is to highlight reliable, bioselective compounds sharing flavonoidic scaffolds in HTS experiments. A filtering scheme was applied to remove undesired flavonoids (and related compounds) from confirmatory PubChem bioassays. A number of 433 compounds addressing various protein targets form part of the collection of bioselective flavonoids and related compounds (ColBioS-FlavRC). With an additional set of 2908 inactive related compounds, ColBioS-FlavRC offers the grounds for method optimization and validation. We exemplified the use of ColBioS-FlavRC by pharmacophore modeling, subsequently (externally) validated for virtual screening purposes. The early enrichment capabilities of the pharmacophore hypotheses were measured by means of the median exponential retriever operating curve enrichment (MeROCE), a suited metric in comparative evaluations of virtual screening methods. ColBioS-FlavRC is available in Supporting Information and is freely accessible for further studies.
    Journal of Chemical Information and Modeling 07/2014; 54(8):2360−2370.
  • [Show abstract] [Hide abstract]
    ABSTRACT: Carbohydrates play an immense role in different aspects of life. NMR spectroscopy is a major and the most powerful tool for investigation of these compounds. Nowadays, progress in computational procedures has opened up novel opportunities, which gave an impulse to develop new instruments intended to make the research simpler and more efficient. In this paper, we present a new approach to simulate 13C NMR chemical shifts of carbohydrates. The approach is suitable for any atomic observables, which could be stored in a database. The method is based on sequential generalization of the chemical surrounding of the atom under prediction and heuristic averaging of database data. Unlike existing applications, the generalization schema is tuned for carbohydrates, including those containing phosphates, amino acids, alditols, and other non-carbohydrate constituents. It was implemented in the GODESS (Glycan-Optimized Dual Empirical Spectrum Simulation) software freely available on the Internet. In the field of carbohydrates, our approach was shown to outperform all other existing methods of NMR spectrum prediction (including quantum mechanical calculations) in accuracy. Only this approach supports NMR spectrum simulation for a number of structural features in polymeric structures.
    Journal of Chemical Information and Modeling 07/2014;
  • [Show abstract] [Hide abstract]
    ABSTRACT: Free energy prediction of ligand binding to macromolecules using explicit solvent molecular dynamics (MD) simulations is computationally very expensive. Recently, we reported a linear correlation between the binding free energy obtained via umbrella sampling (US) versus the rupture force from steered molecular dynamics (SMD) simulations for epigallocatechin-3-gallate (EGCG) binding to α-helical rich keratin. This linear correlation suggests a potential route for fast free-energy predictions using SMD alone. In this work, the generality of the linear correlation is further tested for several ligands interacting with the α-helical motif of keratin. These molecules have significantly varying properties i.e. octanol/water partition coefficient (logP), and/or overall charges (oleic acid, catechin, Fe(2+), citric acid, hydrogen citrate, dihydrogen citrate and citrate). Using the constant loading rate of our previous study of the keratin-EGCG system, we observe that the linear correlation for keratin-EGCG can be extended to other uncharged molecules where interactions are governed by hydrogen bonds and/or a combination of hydrogen bonds and hydrophobic forces. For molecules where interactions with the keratin helix are governed primarily by electrostatics between charged molecules, a second, alternative linear correlation model is derived. Whilst further investigations are needed to expand the molecular space and build a fully predictive model, the current approach represents a promising methodology for fast free energy predictions based on short SMD simulations (requiring picoseconds to nanoseconds of sampling) for defined biomolecular systems.
    Journal of Chemical Information and Modeling 07/2014;
  • [Show abstract] [Hide abstract]
    ABSTRACT: Imidazoline ligands in I2-type binding sites in the brain alter monoamine turnover and release. One example of an I2 binding site characterized by binding studies, kinetics, and crystal structure has been described in monoamine oxidase B (MAO B). MAO A also binds imidazolines but has a different active site structure. Docking and molecular dynamics were used to explore how 2-(2-benzofuranyl)-2-imidazoline hydrochloride (2-BFI) binds to MAO A and to explain why tranylcypromine increases tight binding to MAO B. The energy for 2-BFI binding to MAO A was comparable to that for tranylcypromine-modified MAO B, but the location of 2-BFI in the MAO A could be anywhere in the monopartite substrate cavity. Binding to the tranylcypromine-modified MAO B was with high affinity and in the entrance cavity as in the crystal structure, but the energies of interaction with the native MAO B were less favorable. Molecular dynamics revealed that the entrance cavity of MAO B after tranylcypromine modification is both smaller and less flexible. This change in the presence of tranylcypromine may be responsible for the greater affinity of tranylcypromine-modified MAO B for imidazoline ligands.
    Journal of Chemical Information and Modeling 07/2014;
  • [Show abstract] [Hide abstract]
    ABSTRACT: Protein-peptide interactions are prevalent and play essential roles in many living activities. Peptides recognize their protein partners by direct non-bonded interactions and indirect adjustment of conformations. Although processes of protein-peptide recognition have been comprehensively studied in both sequences and structures recently, flexibility of peptides and the configuration entropy penalty in recognition did not get enough attention. In this study, 20 protein-peptide complexes and their corresponding unbound peptides were investigated by molecular dynamics simulations. Energy analysis revealed that configurational entropy penalty introduced by restriction of the degrees of freedom of peptides in indirect readout process of protein-peptide recognition is significant. Configurational entropy penalty has become the main content of the indirect readout energy in protein-peptide recognition instead of deformation energy which is the main source of the indirect readout energy in classical biomolecular recognition phenomena, such as protein-DNA binding. These results provide us a better understanding of protein-peptide recognition and give us some implications in peptide ligand design.
    Journal of Chemical Information and Modeling 07/2014;
  • [Show abstract] [Hide abstract]
    ABSTRACT: Bioisosteric replacement plays an important role in medicinal chemistry by keeping the biological activity of a molecule while changing either its core scaffold or substituents, thereby facilitating lead optimization and patenting. Bioisosteres are classically chosen in order to keep the main pharmacophoric moieties of the substructure to replace. However, notably when changing a scaffold, no attention is usually paid as whether all atoms of the reference scaffold are equally important for binding to the desired target. We herewith propose a novel database for bioisosteric replacement (scPDBFrag), capitalizing on our recently published structure-based approach to scaffold hopping, focusing on interaction pattern graphs. Protein-bound ligands are first fragmented and the interaction of the corresponding fragments with their protein environment computed-on-the-fly. Using an in-house developed graph alignment tool, interaction patterns graphs can be compared, aligned and sorted by decreasing similarity to any reference. In the herein presented sc-PDB-Frag database (http://bioinfo-pharma.u-strasbg.fr/scPDBFrag), fragments, interaction patterns, alignments and pair-wise similarity scores have been extracted from the sc-PDB database of 8077 druggable protein-ligand complexes and further stored in a relational database. We herewith present the database, its web implementation and procedures for identifying true bioisosteric replacements based on conserved interaction patterns.
    Journal of Chemical Information and Modeling 07/2014;
  • [Show abstract] [Hide abstract]
    ABSTRACT: Molecular dynamic (MD) based MM-PB/SA calculation (MD-PB/SA) was widely used to estimate binding free energy for receptor-ligand complex. While numerous reports focused on assessing accuracy and efficiency, fewer studies paid attention to the performance in lead discovery. In the present study, we reported a critical evaluation of MD-PB/SA in the hierarchical virtual screening (HVS) both theoretically and practically. It's shown that based on native poses, MD-PB/SA could be well applied to predict the relative binding energy for both congeneric and diverse ligands for different protein targets. However, there's limitation for MD-PB/SA to distinguish the native pose of one ligand from the artificial pose of another when the huge difference exists between two molecules. By combining physics-based scoring function with knowledge-based structural filter, we improved the predictability and validated the practical use of MD-PB/SA in lead discovery by identifying novel inhibitors of p38 MAP kinase. We also expanded our study to other protein targets such like HIV-1 RT and NA to assess the general validity of MD-PB/SA.
    Journal of Chemical Information and Modeling 06/2014;
  • [Show abstract] [Hide abstract]
    ABSTRACT: Parkinson's disease is the second most common neurodegenerative disorder, for which no cure or disease-modifying therapies exist. It is evident that mechanisms impairing mitochondrial dynamics will damage cell signaling pathways, leading to neuronal death that manifests as Parkinson's disease. Dynamin related protein1, a highly conserved profission protein that catalyzes the process of mitochondrial fission, is also associated with the excessive fragmentation of mitochondria, impaired mitochondrial dynamics and cell death. Hence, Dynamin related protein1 has emerged as a key therapeutic target for diseases involving mitochondrial dysfunction. In this work, we employed a relatively novel and integrated computational strategy to identify a cryptic binding site of Dynamin related protein1 and exploited the predicted site in the rational drug designing process. This novel approach yielded three potential inhibitors, and all of them were evaluated for their neuroprotective efficacy in C. elegans model of Parkinson's disease.
    Journal of Chemical Information and Modeling 06/2014;
  • Journal of Chemical Information and Modeling 06/2014;
  • [Show abstract] [Hide abstract]
    ABSTRACT: The derivation and optimization of most energy terms in modern force fields are aided by automated computational tools. It is therefore important to have algorithms to rapidly and precisely train large numbers of interconnected parameters to allow investigators to make better decisions about the content of molecular models. In particular, the traditional approach to deriving dihedral parameters has been a least squares fit to target conformational energies through variational optimization strategies. We present a computational approach for simultaneously fitting force field dihedral amplitudes and phase constants which is analytic within the scope of the data set. This approach completes the optimal molecular mechanics representation of a quantum mechanical potential energy surface in a single linear least-squares fit by recasting the dihedral potential into a linear function in the parameters. We compare the resulting method to a genetic algorithm in terms of computational time and quality of fit for two simple molecules. As suggested in previous studies, arbitrary dihedral phases are only necessary when modeling chiral molecules, which include more than half of drugs currently in use, so we also examined a dihedral parameterization case for the drug amoxicillin and one of its stereoisomers where the target dihedral includes a chiral center. Asymmetric dihedral phases are needed in these types of cases to properly represent the quantum mechanical energy surface and to differentiate between stereoisomers about the chiral center.
    Journal of Chemical Information and Modeling 06/2014;

Related Journals