Current Computer - Aided Drug Design (CURR COMPUT-AID DRUG)

Publications in this journal

• Article: Molecular Docking and ADME studies of Natural compounds of Agarwood oil for Topical Anti-Inflammatory activity.

  Dharmendra K Yadav, Vipin Mudgal, Jyoti Agrawal, Anil K Maurya, Dnyaneshwar U Bawankule, Chandan S Chanotiya, Feroz Khan, Sanjog T Thul
  [show abstract] [hide abstract]
  ABSTRACT: Aquilaria agallocha Roxb. family, Thymelaeaceae, is an evergreen plant of South-East Asia, commonly described as aloe wood or agarwood. Traditionally, the bark, root and heartwood are used for their medicinal properties as a folk medicine for hundreds of years. Chemical analyses revealed that the bulk of the oil is constituted by agarospirol (12.5%), jinkoh-eremol (11.8%) and hinesol (8.9%) as major contributor. In the present work, a QSAR model for anti-inflammatory activity of 10-epi-γ-Eudesmol, jinkoh-eremol, agarospirol and other compounds has been developed by multiple linear regression method. The r2 and rCV2 of a model were 0.89 and 0.81 respectively. In silico molecular docking study suggests that compound 10-epi-γ-Eudesmol, jinkoh-eremol and agarospirol are preferentially more active than other identified compounds with strong binding affinity to major anti-inflammatory and immunomodulatory receptors. The oil displayed a significant and dose dependent reduction of 12-O-tetradecanoylphorobol-13 acetate (TPA)-induced ear edema and MDA activity when compared with vehicle treated mice. Pro-inflammatory cytokines (IL-1β, IL-6 and TNF-α) were also reduced significantly in a dose dependent manner in all the TPA treated groups as compared to control. The present study indicates that agarwood oil significantly reduced the skin thickness, ear weight, oxidative stress and pro-inflammatory cytokines production in TPA-induced mouse ear inflammation model and contributed towards validation of its traditional use to treat inflammation related ailments.
  Current Computer - Aided Drug Design 02/2013;
• Article: QSAR study of Curcumine Derivatives as HIV-1 Integrase Inhibitors.

  Pawan Gupta, Anju Sharma, Prabha Garg, Nilanjan Roy
  [show abstract] [hide abstract]
  ABSTRACT: A QSAR study was performed on curcumine derivatives as HIV-1 integrase inhibitors using multiple linear regression. The statistically significant model was developed with squared correlation coefficients (r2) 0.891 and cross validated r2 (r2cv) 0.825. The developed model revealed that electronic, shape, size, geometry, substitution's information and hydrophilicity was important atomic properties for determining the inhibitory activity of these molecules. The model was also tested successfully for external validation (r2pred = 0.849) as well as Tropsha's test for model predictability. Furthermore, the domain analysis was carried out to evaluate the prediction reliability of external set molecules model. The model was statistically robust and had good predictive power which can be successfully utilized for screening of new molecules.
  Current Computer - Aided Drug Design 11/2012;
• Article: Artificial Neural Networks Based on CODES Descriptors in Pharmacology: Identification of Novel Trypanocidal Drugs against Chagas Disease.

  Angela Guerra, Pedro Gonzalez-Naranjo, Nuria E Campillo, Hugo Cerecetto, Mercedes González, Juan A Páez
  [show abstract] [hide abstract]
  ABSTRACT: A supervised artificial neural network model has been developed for the accurate prediction of the anti-T. cruzi activity of heterogeneous series of compounds. A representative set of 72 compounds of wide structural diversity was chosen in this study. The definition of the molecules was achieved from a un supervised neural network using a new methodology, CODES program. This program codifies each molecule into a set of numerical parameters taking into account exclusively its chemical structure. The final model shows high average accuracy of 84% (training performance) and predictability of 77% (external validation performance) for the 4:4:1 architecture net with different training set and external prediction test. This approach using CODES methodology represents a useful tool for the prediction of pharmacological properties. CODES© is available free of charge for academic institutions.
  Current Computer - Aided Drug Design 11/2012;
• Article: Quantum Mechanical Scoring: Structural and Energetic Insights into Cyclin-dependent Kinase 2 Inhibition by Pyrazolo[1,5-a]pyrimidines.

  Pathik S Brahmkshatriya, Petr Dobeš, Jindřich Fanfrlík, Jan Rezáč, Kamil Paruch, Agnieszka Bronowska, Martin Lepšík, Pavel Hobza
  [show abstract] [hide abstract]
  ABSTRACT: A quantum mechanics (QM)-based scoring function has been applied to complexes of cyclin-dependent kinase 2 (CDK2) and thirty-one pyrazolo[1,5-a]pyrimidine-based inhibitors and their bioisosteres. A hybrid three-layer QM/MM setup (DFT-D/PM6-D3H4X/AMBER in generalized Born solvent) was used here for the first time as an extension of our previous full QM and SQM/MM (SQM means semiempirical QM) approaches. Two approaches to obtain the structures of the CDK2/inhibitor complexes were examined: i) building the modifications from one X-ray structure available coupled with a conformational search and ii) docking the compounds into CDK2. The QM-based scoring entailed a QM/SQM/MM optimization followed by calculations of the binding scores which were subsequently correlated with the experimental binding free energies. The correlation for the building protocol was good (r2 = 0.64, predictive index = 0.81), whereas the docking approach failed. A decomposition of the interaction energies to ligand fragments enabled us to rationalize the differences in the binding affinities. In conclusion, we have developed and refined a QM-based scoring protocol and successfully applied it to reproduce the binding affinities in congeneric series of CDK2 inhibitors and to rationalize their potency. We thus propose that such a tool can be used in computer-aided rational drug design.
  Current Computer - Aided Drug Design 11/2012;
• Article: QSAR modeling for the antimalarial activity of 1, 4-naphthoquinonyl derivatives as potential antimalarial agents.

  Feng Luan, Xuan Xu, Maria Natália Dias Soeiro Cordeiro, Huitao Liu, Xiaoyun Zhang
  [show abstract] [hide abstract]
  ABSTRACT: Malaria has been known as one of the major causes of morbidity and mortality on a large scale in tropical countries until now. In the past decades, many scientific groups have focused their attention on looking for ideal drugs to this diseases. So far, this research area is still a hot topic. In the present study, the antimalarial activity of 1, 4-naphthoquinonyl derivatives was modeled by linear and nonlinear statistical methods, that is to say, by forward stepwise multilinear regression (MLR) and radial basis function neural networks (RBFNN). The derived QSAR models have been statistically validated both internally - by means of the Leave One Out (LOO) and Leave Many Out (LMO) cross-validation, and Y-scrambling techniques, as well as externally (by means of an external test set). The statistical parameters provided by the MLR model were R2 =0.7876, LOOq2 =0.7068, RMS =0.3377, R02 =0.7876, k =1.0000 for the training set, and R2 =0.7648, q2ext =0.7597, RMS=0.2556, R02=0.7598, k=1.0417 for the external test set. The RBFNN model gave the following statistical results, namely: R2=0.8338, LOOq2=0.5869, RMS=0.2781, R02 = 0.8335, k=1.0000 for the training set, and R2 =0.7586, q2ext =0.7189, RMS=0.2788, R02=0.7129, k=1.0284 for the external test set. Overall, these results suggest that the QSAR MLR-based model is a simple, reliable, credible and fast tool for the prediction and virtual screening of 1, 4-naphoquinone derivatives with high antimalarial activity. In addition, the energies of the highest occupied molecular orbital were found to have high correlation with the activity.
  Current Computer - Aided Drug Design 11/2012;
• Article: Experimental and Computational Studies on the Inhibition of Acetylcholinesterase by Curcumin and Some of its Derivatives.

  Verónica Tello-Franco, María Concepción Lozada-García, Manuel Soriano-García
  [show abstract] [hide abstract]
  ABSTRACT: Recent studies have demonstrated several biological activities of curcumin with therapeutic potential against Alzheimer's disease, among them the inhibition of the enzyme acetylcholinesterase (AChE). Aiming at identifying the chemical features relevant for this activity, the inhibition of curcumin and a set of 7 derivatives against AChE of E. electricus was measured. These derivatives presented lower activity than curcumin, allowing for the identification of possible unfavorable enzyme-inhibitor interactions. Our computational approach was to dock the molecules to the active site of AChE, followed by an analysis of hydrogen bonds and close contacts to relevant aromatic amino acid residues. To account for inhibitory activity, we sought to define the common structural features between known acetylcholinesterase inhibitors and the tested derivatives. A pharmacophore model was generated, which consisted of two hydrophobic, one aromatic and one hydrogen bond acceptor features. We conclude that the presence of two aromatic rings and the distance between them, allows curcumin and its derivatives to favorably interact with both the quaternary and peripheral sites of AChE. Hydrogen bonds can be formed with the quaternary and acyl sites, which should further stabilize the complex. The acylation of the hydroxyl groups and the reduction of the conjugated double bonds lowered the inhibitory activity, pointing to the modification of the keto-enol moiety as the best alternative for the design of more potent curcumin derivatives as acetylcholinesterase inhibitors.
  Current Computer - Aided Drug Design 10/2012;
• Article: Efficacy Prediction of Jamu Formulations by PLS Modeling.

  Farit M Afendi, Latifah K Darusman, Aki Hirai Morita, Md Altaf-Ul-Amin, Hiroki Takahashi, Kensuke Nakamura, Ken Tanaka, Shigehiko Kanaya
  [show abstract] [hide abstract]
  ABSTRACT: Indonesian herbal medicines made from mixtures of several plants are called "Jamu." The efficacy of a particular Jamu is determined by its ingredients i.e. the composition of the plants. Thus, we modeled the ingredients of Jamu formulas using Partial Least Squares Discriminant Analysis (PLS-DA) in order to predict their efficacy. The plants used in each Jamu medicine served as the predictors, whereas the efficacy of each Jamu provided the responses. Utilizing response predictions obtained from PLS-DA, we predicted the efficacies of Jamu formulations using two methods: maximum response prediction and maximum probability. In predictions of Jamu efficacy, the maximum response prediction method produced a smaller error than that the maximum probability method. Furthermore, utilizing the PLS-DA coefficient matrix, we determined the efficacy for which a plant is most useful, based on its largest coefficients.
  Current Computer - Aided Drug Design 10/2012;
• Article: Computational Modeling Of Environmentally Responsive Hydrogels (ERH) For Drug Delivery System.

  P K Krishnan Namboori, U P Ranjini, Asha A Manakadan, Anila Jose, K S Silvipriya, N Belzik, O M Deepak
  [show abstract] [hide abstract]
  ABSTRACT: The present work aims at computational analysis of environmentally responsive hydrogels with enormous prospective in the formulation aspect of drug delivery systems. The drug delivery potential of hydrogels to the targets is owing to the specific stimuli responsive nature of the hydrogels. The environmental factors looked upon in the study are changes in pH, alteration of temperature and glucose concentration rise originated in the body as a result of various disease conditions. Polymers, synthetic polypeptides and dendrimers have been used in the present work to study the feasibility of drug delivery. The computational methods have been used to formulate polymer properties, pharmacokinetics and toxicity studies. Diverse interactions approximating electrostatic, hydrophobic and hydrogen bond interactions acquire place during incorporation of drugs within the polymer and dendrimers. The covalent and electrostatic interactions between a drug and the surface of polymer and dendrimer have been analyzed. The docking interaction studies have been performed and the best polymer and dendrimer complex have been selected based on the docking score, binding energy and interaction energy with the drugs. G5 generation of poly amidoamine dendrimers and poly N-N-diethyl acrylamide (PDEAAM) have been identified as most suitable stimuli-responsive effective drug carriers for anti diabetic drugs and diuretics. Favorable results have been obtained while using poly acrylic acid (PAA) for corticosteroids and polylysine for diabetic drugs. ConA protein along with poly aspartic acid also showed good results.
  Current Computer - Aided Drug Design 10/2012;
• Article: Molecular Determinants of the Bacterial Resistance to Fluoroquinolones: A Computational Study.

  Cecylia Severin Lupala, Patricia Gomez-Gutierrez, Juan J Perez
  [show abstract] [hide abstract]
  ABSTRACT: Quinolones constitute a large class of antibacterial agents whose action is mediated through the formation of a ternary complex with DNA and either, DNA Gyrase or topoisomerase IV, resulting in the inhibition of DNA replication. In order to get a deeper insight into the features of the complex formation, we carried out docking studies of fifteen diverse quinolones to the cleaved topoisomerase IV-DNA complex. Docking studies were performed using the crystal structures of the cleaved complex with levofloxacin and moxifloxacin (pdb entries 3K9F and 2XKK, respectively) using the GOLD software. Ligands dock in positions similar to those of the crystal structures. Analysis of the results reveals that bound quinolones appear intercalated between the two nucleotides that are involved in the DNA cleavage and exhibit hydrogen bonds with Arg117 and, the latter mediated though a water molecule. Arg117 has not been described to be involved in resistance, since it is putatively involved in the enzymatic reaction and its mutation would be lethal for the organism. Mutants of Ser79 exhibit resistance to quinolones which can be explained by the loss of an important anchoring point. Interestingly, quinolone resistance observed in Asp83 mutants cannot be explained directly on the basis of the loss of a direct interaction, but could be explained on the basis of its involvement at the entrance of the ligands to their binding pocket since the residue is located at the mouth of the pocket. The results of the present study suggest that the 4-keto and 3-carboxyl groups of the fluoroquinolones bind a Mg2+ before binding to the cleaved topoisomarase IV-DNA complex and use Asp83 for entry into the binding pocket. Accordingly, mutations that do not conserve the binding capacity for the quinolone-Mg2+ complex will prevent the binding of this class of ligands. The results we present here are also compared with the structure of PD0305970 a 2,4-dione active against the Ser79 and Asp83 mutants.
  Current Computer - Aided Drug Design 10/2012;
• Article: Post-Docking Optimization and Analysis of Protein-Ligand Interactions of Estrogen Receptor Alpha using AMMOS Software.

  Tania Pencheva, Dessislava Jereva, Maria A Miteva, Ilza Pajeva
  [show abstract] [hide abstract]
  ABSTRACT: Understanding protein-ligand interactions is a critical step in rational drug design/virtual ligand screening. In this work we applied the AMMOS_ProtLig software for post-docking optimization of estrogen receptor alpha complexes generated after virtual ligand screening protocol. Using MOE software we identified the ligand-receptor interactions in the optimized complexes at different levels of protein flexibility and compared them to the experimentally observed interactions. We analyzed in details the binding sites of three X-ray complexes of the same receptor and identified the key residues for the protein-ligand interactions. The complexes were further processed with AMMOS_ProtLig and the interactions in the predicted poses were compared to those observed in the X-ray structures. The effect of employing different levels of flexibility was analyzed. The results confirmed the AMMOS_ProtLig applicability as a helpful post-docking optimization tool for virtual ligand screening of estrogen receptors.
  Current Computer - Aided Drug Design 10/2012;
• Article: Topological and electrotopological descriptors of molecules: fundamental principles and applications to computer aided molecular design--part II. A special issue of Current Computer-Aided Drug Design honoring Professor Lemont B. Kier on his eightieth birthday.

  Subhash C Basak, Guillermo Restrepo
  Current Computer - Aided Drug Design 06/2012; 8(3):171.
• Article: Quantum pharmacology for infectious diseases: a molecular connectivity approach.

  Shailza Singh
  [show abstract] [hide abstract]
  ABSTRACT: Infectious diseases are a major cause of global health, economic and social problems. Relationship between the infectious diseases and drugs designed to combat them can be understood by the Quantum Pharmacology approach. Quantum pharmacology which is an amalgamation of chemistry, quantum mechanics and computer modeling aims to understand the structure activity relationship of a drug. As compared to the classical MM, the hybrid QM/MM approach which takes into account the quantum mechanics along with the molecular mechanics facilitates the simulation of biological structures with greater accuracy and speed. This review highlights the importance of quantum mechanics for a better understanding of molecular systems and QSAR studies.
  Current Computer - Aided Drug Design 06/2012; 8(3):249-54.
• Article: Introduction to molecular topology: basic concepts and application to drug design

  Jorge Gálvez, María Gálvez-Llompart, Ramón García-Domenech
  [show abstract] [hide abstract]
  ABSTRACT: In this review it is dealt the use of molecular topology (MT) in the selection and design of new drugs. After an introduction of the actual methods used for drug design, the basic concepts of MT are defined, including examples of calculation of topological indices, which are numerical descriptors of molecular structures. The goal is making this calculation familiar to the potential students and allowing a straightforward comprehension of the topic. Finally, the achievements obtained in this field are detailed, so that the reader can figure out the great interest of this approach.
  Current Computer - Aided Drug Design 06/2012; 8(3):196-223.
• Article: Structure-Based Development of Antagonists for Chemokine Receptor CXCR4.

  Chongqian Zhang, Tingjun Hou, Zhiwei Feng, Youyong Li
  [show abstract] [hide abstract]
  ABSTRACT: The C-X-C chemokine receptor-4(CXCR4) is a G-protein coupled receptor (GPCR) which belongs to the family I GPCR or rhodopin-like GPCR family. CXCR4 plays a crucial role as a co-receptor with CCR5 for HIV-1 anchoring to mammalian cell membrane, and is implicated in cancer metastasis and inflammation. Recently, crystal structure of human CXCR4 receptor was reported, which facilitates the structure-based drug discovery of CXCR4 significantly. Here we summarize the structure feature of C-X-C chemokine and its difference from other rhodopsin-like GPCR family, the impact of recent crystal structure on CXCR4 drug development, the available active compounds for CXCR4 receptor, SAR studies of the available active compounds, the recognition mechanism of the inhibitors of CXCR4 receptor (molecular docking results and molecular dynamics results), which illustrates the interaction between the inhibitors and critical residues of CXCR4, and the outlook of drug development for CXCR4 receptor.
  Current Computer - Aided Drug Design 06/2012;
• Article: An integrated drug development approach applying topological descriptors.

  Alan Talevi, Carolina L Bellera, Mauricio Di Ianni, Pablo R Duchowicz, Luis E Bruno-Blanch, Eduardo A Castro
  [show abstract] [hide abstract]
  ABSTRACT: We describe the opportunities posed by computer-assisted drug design in the light of two aspects of the current drug discovery scenario: the decline of innovation due to high attrition rates at clinical stage of development and the combinatorial explosion emerging from exponential growth of feasible small molecules and genome and proteome exploration. We present an overview of recent reports from our group in the field of rational drug development, by using topological descriptors (either alone, or in combination with different 3D approaches) and a diversity of modeling techniques such as Linear Discriminant Analysis and the Replacement Method. Modeling efforts aimed at the integrated prediction of several significant molecular properties in the field of drug discovery, such as pharmacological activity, aqueous solubility, human intestinal permeability and affinity to P-glycoprotein (ABCB1, MDR1) are reviewed. The suitability of conformation-independent descriptors to explore large chemical repositories is highlighted, as well as the opportunities posed by in silico guided drug repurposing.
  Current Computer - Aided Drug Design 06/2012; 8(3):172-81.
• Article: Integrated Ligand Based Pharmacophore Model Derived from Diverse FAAH Covalent Ligand Classes.

  Lingling Shen, Hongwei Huang, Alexandros Makriyannis, L S Fisher
  [show abstract] [hide abstract]
  ABSTRACT: 3D pharmacophore modeling is an important computational methodology for ligand-enzyme binding interactions in drug discovery. More specifically, a consensus pharmacophore model derived from diverse ligands is a key determinant upon which the prediction power of computational models is based for designing novel ligands. In this work, by merging the important pharmacophore features based on four classes of covalent FAAH ligands, and then integrating the exclusion volume spheres derived from the crystal structure, we created for the first time an integrated FAAH pharmacophore model to describe the ligand-enzyme binding interactions. This new integrated FAAH pharmacophore model can correctly predict the covalent ligand binding mode, which correlates with the SAR data. The study is expected to provide insights into novel covalent ligand-FAAH binding interactions, and facilitate the design of covalent ligands against FAAH.
  Current Computer - Aided Drug Design 06/2012;
• Article: Chemometric modeling of 5-Phenylthiophenecarboxylic acid derivatives as anti-rheumatic agents.

  Nilanjan Adhikari, Dhritiman Jana, Amit K Halder, Chanchal Mondal, Milan K Maiti, Tarun Jha
  [show abstract] [hide abstract]
  ABSTRACT: Arthritis involves joint inflammation, synovial proliferation and damage of cartilage. Interleukin-1 undergoes acute and chronic inflammatory mechanisms of arthritis. Non-steroidal anti-inflammatory drugs can produce symptomatic relief but cannot act through mechanisms of arthritis. Diseases modifying anti-rheumatoid drugs reduce the symptoms of arthritis like decrease in pain and disability score, reduction of swollen joints, articular index and serum concentration of acute phage proteins. Recently, some literature references are obtained on molecular modeling of antirheumatic agents. We have tried chemometric modeling through 2D-QSAR studies on a dataset of fifty-one compounds out of which forty-four 5-Phenylthiophenecarboxylic acid derivatives have IL-1 inhibitory activity and forty-six 5-Phenylthiophenecarboxylic acid derivatives have %AIA suppressive activity. The work was done to find out the structural requirements of these anti-rheumatic agents. 2D QSAR models were generated by 2D and 3D descriptors by using multiple linear regression and partial least square method where IL-1 antagonism was considered as the biological activity parameter. Statistically significant models were developed on the training set developed by k-means cluster analysis. Sterimol parameters, electronic interaction at atom number 9, 2D autocorrelation descriptors, information content descriptor, average connectivity index chi-3, radial distribution function, Balaban 3D index and 3D-MoRSE descriptors were found to play crucial roles to modulate IL-1 inhibitory activity. 2D autocorrelation descriptors like Broto-Moreau autocorrelation of topological structure-lag 3 weighted by atomic van der Waals volumes, Geary autocorrelation-lag 7 associated with weighted atomic Sanderson electronegativities and 3D-MoRSE descriptors like 3D-MoRSE-signal 22 related to atomic van der Waals volumes, 3D-MoRSE-signal 28 related to atomic van der Waals volumes and 3D-MoRSE-signal 9 which was unweighted, were found to play important roles to model %AIA suppressive activity.
  Current Computer - Aided Drug Design 06/2012; 8(3):182-95.
• Article: Structure- and Ligand-Based Structure-Activity Relationships for a Series of Inhibitors of Aldolase.

  Leonardo G Ferreira, Adriano D Andricopulo
  [show abstract] [hide abstract]
  ABSTRACT: Aldolase has emerged as a promising molecular target for the treatment of human African trypanosomiasis. Over the last years, due to the increasing number of patients infected with Trypanosoma brucei, there is an urgent need for new drugs to treat this neglected disease. In the present study, two-dimensional fragment-based quantitative-structure activity relationship (QSAR) models were generated for a series of inhibitors of aldolase. Through the application of leave-one-out and leave-many-out cross-validation procedures, significant correlation coefficients were obtained (r2 = 0.98 and q2 = 0.77) as an indication of the statistical internal and external consistency of the models. The best model was employed to predict pKi values for a series of test set compounds, and the predicted values were in good agreement with the experimental results, showing the power of the model for untested compounds. Moreover, structure-based molecular modeling studies were performed to investigate the binding mode of the inhibitors in the active site of the parasitic target enzyme. The structural and QSAR results provided useful molecular information for the design of new aldolase inhibitors within this structural class.
  Current Computer - Aided Drug Design 06/2012;