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Special Issue "Big Data Analysis and QSAR/QSPR Research in Chemistry, Bio-Medical, and Network Sciences"

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Physical Chemistry, Theoretical and Computational Chemistry".

Deadline for manuscript submissions: closed (30 April 2016)

Special Issue Editors

Guest Editor
Prof. Dr. Humberto González-Díaz

1 Department of Organic Chemistry II, University of the Basque Country UPV/EHU, 48940, Bizkaia, Leioa, Sarriena w/n, Spain
2 IKERBASQUE, Basque Foundation for Science, 48011, Bilbao, Bizkaia, Spain
Website1 | Website2 | E-Mail
Phone: +3494 601 3547
Fax: +3494 601 2748
Interests: computational chemistry, cheminformatics; data analysis; network science; computational materials and nanosciences
Guest Editor
Prof. Dr. Roberto Todeschini

Milano Chemometrics and QSAR Research Group, Department of Environmental Sciences, University of Milano-Bicocca, Milano 20126, Italy
Website | E-Mail
Phone: +39 02 64482820
Fax: +39 02 64482839
Interests: chemometric, QSAR/QSPR, multi-criteria decision making, molecular descriptors, software development
Guest Editor
Prof. Dr. Alejandro Pazos Sierra

CITIC, INIBIC, University of Coruña UDC, Campus de Elviña s/n, 15071, A Coruña, Spain
Website1 | Website2 | E-Mail
Phone: +34 981 167 000
Fax: +34 981 167 000
Interests: big data analysis; machine learning, artificial intelligence; bioinformatics; cheminformatics
Co-Guest Editor
Dr. Sonia Arrasate Gil

Department of Organic Chemistry II, University of the Basque Country (UPV/EHU), Leioa, Sarriena w/n, Bizkaia
Website | E-Mail
Interests: organic chemistry; organic synthesis; chemical catalysis; computational chemistry

Special Issue Information

Dear Colleagues,

There is a steady increasing necessity of multidisciplinary collaborations in molecular science between experimentalists and theoretical scientists, as well as among theoretical scientists from different fields. One of the more important forces driving this necessity is the accumulation of large amounts of data as results of important advances in Chemometrics and Molecular Sciences Experimental Techniques of data acquisition in general.

In this context, we decided to create[MD1] a new scientific conference to promote the scientific synergies expressed earlier. MOL2NET (the conference's running title) will be held from 15–30 December, 2015, on the SciForum platform. The official website of the conference is: http://sciforum.net/conference/mol2net-1. Represented disciplines will encompass the molecular and biomedical sciences, social networks analysis, and beyond. More specifically, this conference aims to promote scientific synergies between groups of experimental molecular and bio-medical scientists. Relevant fields include chemistry, pharmacology, cancer research, proteomics, the neurosciences, the nanosciences, and epidemiology. Moreover, the conference welcomes computational and social sciences experts from different areas, such as computational chemistry, bioinformatics, social networks analysis, big data predictive analytics, biostatistics, etc. The full title of this conference is the 1st International Conference on Synergies of Experimental Groups of Molecular and Biomedical Sciences with Data, Networks, and Social Sciences Experts. The conference per se is the result of the synergy between the Department of Organic Chemistry, University of Basque Country (UPV/EHU), and IKERBASQUE, Basque Foundation for Sciences, with the Faculty of Informatics, University of Coruña (UDC).

In order to strengthen and spread the outputs of MOL2NET, we decided to be the Guest Editors for one Special Issue. In consonance with the conference, the topic of the issue is: Big Data Analysis and QSAR/QSPR Research in Chemistry, Bio-Medical, and Network Sciences. The issue is focused on the development and application of different theoretical algorithms combining Chemoinformatics, Computational Chemistry, Bioinformatics, Data Analysis, and Network Science methods. Submissions of other authors that do not attend the conference are also welcome. Accepted papers will be published in the International Journal of Molecular Science (IJMS), which is an open access publication journal of MDPI, in the field of Molecular and Biomedical Sciences (http://www.mdpi.com/journal/ijms).

Prof. Dr. Humberto González-Díaz
(IKERBASQUE Senior Professor)
Prof. Dr. Alejandro Pazos Sierra
Prof. Dr. Roberto Todeschini
Guest Editors

Dr. Sonia Arrasate Gil
Co-Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Molecular Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Big Data Analysis in Chemometrics
  • QSPR Chemoinformatics models of Chemical Reactivity
  • Computer-Aided Drug Discovery (CADD) with QSAR models
  • DNA/Protein Quantitative Sequence-Activity Models (QSAM) in Bioinformatics
  • Structure-Property Relationships Analysis of Bio-Molecular Networks
  • Prediction of Drug-Target Interaction Networks
  • Computational Proteomics and Metabolomics
  • Protein Interaction Networks
  • Machine Learning in Cheminformatics

Published Papers (19 papers)

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Editorial

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Open AccessEditorial Data Analysis in Chemistry and Bio-Medical Sciences
Int. J. Mol. Sci. 2016, 17(12), 2105; doi:10.3390/ijms17122105
Received: 10 October 2016 / Revised: 5 December 2016 / Accepted: 7 December 2016 / Published: 14 December 2016
Cited by 1 | PDF Full-text (172 KB) | HTML Full-text | XML Full-text

Research

Jump to: Editorial, Review

Open AccessArticle Conformation-Independent QSPR Approach for the Soil Sorption Coefficient of Heterogeneous Compounds
Int. J. Mol. Sci. 2016, 17(8), 1247; doi:10.3390/ijms17081247
Received: 30 April 2016 / Revised: 5 July 2016 / Accepted: 22 July 2016 / Published: 3 August 2016
Cited by 5 | PDF Full-text (717 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
We predict the soil sorption coefficient for a heterogeneous set of 643 organic non-ionic compounds by means of Quantitative Structure-Property Relationships (QSPR). A conformation-independent representation of the chemical structure is established. The 17,538 molecular descriptors derived with PaDEL and EPI Suite softwares are
[...] Read more.
We predict the soil sorption coefficient for a heterogeneous set of 643 organic non-ionic compounds by means of Quantitative Structure-Property Relationships (QSPR). A conformation-independent representation of the chemical structure is established. The 17,538 molecular descriptors derived with PaDEL and EPI Suite softwares are simultaneously analyzed through linear regressions obtained with the Replacement Method variable subset selection technique. The best predictive three-descriptors QSPR is developed on a reduced training set of 93 chemicals, having an acceptable predictive capability on 550 test set compounds. We also establish a model with a single optimal descriptor derived from CORAL freeware. The present approach compares fairly well with a previously reported one that uses Dragon descriptors. Full article
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Open AccessArticle A Machine Learning Approach for Hot-Spot Detection at Protein-Protein Interfaces
Int. J. Mol. Sci. 2016, 17(8), 1215; doi:10.3390/ijms17081215
Received: 24 May 2016 / Revised: 11 July 2016 / Accepted: 18 July 2016 / Published: 27 July 2016
Cited by 4 | PDF Full-text (527 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Understanding protein-protein interactions is a key challenge in biochemistry. In this work, we describe a more accurate methodology to predict Hot-Spots (HS) in protein-protein interfaces from their native complex structure compared to previous published Machine Learning (ML) techniques. Our model is trained on
[...] Read more.
Understanding protein-protein interactions is a key challenge in biochemistry. In this work, we describe a more accurate methodology to predict Hot-Spots (HS) in protein-protein interfaces from their native complex structure compared to previous published Machine Learning (ML) techniques. Our model is trained on a large number of complexes and on a significantly larger number of different structural- and evolutionary sequence-based features. In particular, we added interface size, type of interaction between residues at the interface of the complex, number of different types of residues at the interface and the Position-Specific Scoring Matrix (PSSM), for a total of 79 features. We used twenty-seven algorithms from a simple linear-based function to support-vector machine models with different cost functions. The best model was achieved by the use of the conditional inference random forest (c-forest) algorithm with a dataset pre-processed by the normalization of features and with up-sampling of the minor class. The method has an overall accuracy of 0.80, an F1-score of 0.73, a sensitivity of 0.76 and a specificity of 0.82 for the independent test set. Full article
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Open AccessArticle Molecular Rearrangement of an Aza-Scorpiand Macrocycle Induced by pH: A Computational Study
Int. J. Mol. Sci. 2016, 17(7), 1131; doi:10.3390/ijms17071131
Received: 1 May 2016 / Revised: 30 June 2016 / Accepted: 7 July 2016 / Published: 14 July 2016
Cited by 2 | PDF Full-text (927 KB) | HTML Full-text | XML Full-text
Abstract
Rearrangements and their control are a hot topic in supramolecular chemistry due to the possibilities that these phenomena open in the design of synthetic receptors and molecular machines. Macrocycle aza-scorpiands constitute an interesting system that can reorganize their spatial structure depending on pH
[...] Read more.
Rearrangements and their control are a hot topic in supramolecular chemistry due to the possibilities that these phenomena open in the design of synthetic receptors and molecular machines. Macrocycle aza-scorpiands constitute an interesting system that can reorganize their spatial structure depending on pH variations or the presence of metal cations. In this study, the relative stabilities of these conformations were predicted computationally by semi-empirical and density functional theory approximations, and the reorganization from closed to open conformations was simulated by using the Monte Carlo multiple minimum method. Full article
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Open AccessArticle Molecular Dynamics Simulation Study of the Selectivity of a Silica Polymer for Ibuprofen
Int. J. Mol. Sci. 2016, 17(7), 1083; doi:10.3390/ijms17071083
Received: 19 May 2016 / Revised: 9 June 2016 / Accepted: 28 June 2016 / Published: 7 July 2016
Cited by 2 | PDF Full-text (1745 KB) | HTML Full-text | XML Full-text
Abstract
In the past few years, the sol-gel polycondensation technique has been increasingly employed with great success as an alternative approach to the preparation of molecularly imprinted materials (MIMs). The main aim of this study was to study, through a series of molecular dynamics
[...] Read more.
In the past few years, the sol-gel polycondensation technique has been increasingly employed with great success as an alternative approach to the preparation of molecularly imprinted materials (MIMs). The main aim of this study was to study, through a series of molecular dynamics (MD) simulations, the selectivity of an imprinted silica xerogel towards a new template—the (±)-2-(P-Isobutylphenyl) propionic acid (Ibuprofen, IBU). We have previously demonstrated the affinity of this silica xerogel toward a similar molecule. In the present study, we simulated the imprinting process occurring in a sol-gel mixture using the Optimized Potentials for Liquid Simulations-All Atom (OPLS-AA) force field, in order to evaluate the selectivity of this xerogel for a template molecule. In addition, for the first time, we have developed and verified a new parameterisation for the Ibuprofen® based on the OPLS-AA framework. To evaluate the selectivity of the polymer, we have employed both the radial distribution functions, interaction energies and cluster analyses. Full article
Open AccessArticle Prognostic Value of Affective Symptoms in First-Admission Psychotic Patients
Int. J. Mol. Sci. 2016, 17(7), 1039; doi:10.3390/ijms17071039
Received: 26 April 2016 / Revised: 22 June 2016 / Accepted: 24 June 2016 / Published: 30 June 2016
Cited by 2 | PDF Full-text (201 KB) | HTML Full-text | XML Full-text
Abstract
Background: Very little research has been conducted in patients with first-episode psychosis using a dimensional approach. Affective dimensional representations might be useful to predict the clinical course and treatment needs in such patients. Methods: Weincluded 112 patients with first-episode psychosis in a longitudinal-prospective
[...] Read more.
Background: Very little research has been conducted in patients with first-episode psychosis using a dimensional approach. Affective dimensional representations might be useful to predict the clinical course and treatment needs in such patients. Methods: Weincluded 112 patients with first-episode psychosis in a longitudinal-prospective study with a five-year follow-up (N = 82). Logistic analyses were performed to determine the predictive factors associated with depressive, manic, activation, and dysphoric dimensions. Results: High scores on the depressive dimension were associated with the best prognosis. On the other hand, high scores on the activation dimension and the manic dimension were associated with a poorer prognosis in terms of relapses. Only the dysphoric dimension was not associated with syndromic or functional prognosis. Conclusion: Ourresults suggest that the pattern of baseline affective symptoms helps to predict the course of psychotic illness. Therefore, the systematic assessment of affective symptoms would enable us to draw important conclusions regarding patients’ prognosis. Interventions for patients with high scores on manic or activation dimensions could be beneficial in decreasing relapses in first-episode psychosis. Full article
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Open AccessArticle Genome-Wide Discriminatory Information Patterns of Cytosine DNA Methylation
Int. J. Mol. Sci. 2016, 17(6), 938; doi:10.3390/ijms17060938
Received: 25 February 2016 / Revised: 16 May 2016 / Accepted: 2 June 2016 / Published: 17 June 2016
Cited by 4 | PDF Full-text (5012 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Cytosine DNA methylation (CDM) is a highly abundant, heritable but reversible chemical modification to the genome. Herein, a machine learning approach was applied to analyze the accumulation of epigenetic marks in methylomes of 152 ecotypes and 85 silencing mutants of Arabidopsis thaliana.
[...] Read more.
Cytosine DNA methylation (CDM) is a highly abundant, heritable but reversible chemical modification to the genome. Herein, a machine learning approach was applied to analyze the accumulation of epigenetic marks in methylomes of 152 ecotypes and 85 silencing mutants of Arabidopsis thaliana. In an information-thermodynamics framework, two measurements were used: (1) the amount of information gained/lost with the CDM changes I R and (2) the uncertainty of not observing a SNP L C R . We hypothesize that epigenetic marks are chromosomal footprints accounting for different ontogenetic and phylogenetic histories of individual populations. A machine learning approach is proposed to verify this hypothesis. Results support the hypothesis by the existence of discriminatory information (DI) patterns of CDM able to discriminate between individuals and between individual subpopulations. The statistical analyses revealed a strong association between the topologies of the structured population of Arabidopsis ecotypes based on I R and on LCR, respectively. A statistical-physical relationship between I R and L C R was also found. Results to date imply that the genome-wide distribution of CDM changes is not only part of the biological signal created by the methylation regulatory machinery, but ensures the stability of the DNA molecule, preserving the integrity of the genetic message under continuous stress from thermal fluctuations in the cell environment. Full article
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Open AccessArticle In Silico Prediction of Cytochrome P450-Drug Interaction: QSARs for CYP3A4 and CYP2C9
Int. J. Mol. Sci. 2016, 17(6), 914; doi:10.3390/ijms17060914
Received: 16 May 2016 / Revised: 1 June 2016 / Accepted: 6 June 2016 / Published: 9 June 2016
Cited by 6 | PDF Full-text (8849 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Cytochromes P450 (CYP) are the main actors in the oxidation of xenobiotics and play a crucial role in drug safety, persistence, bioactivation, and drug-drug/food-drug interaction. This work aims to develop Quantitative Structure-Activity Relationship (QSAR) models to predict the drug interaction with two of
[...] Read more.
Cytochromes P450 (CYP) are the main actors in the oxidation of xenobiotics and play a crucial role in drug safety, persistence, bioactivation, and drug-drug/food-drug interaction. This work aims to develop Quantitative Structure-Activity Relationship (QSAR) models to predict the drug interaction with two of the most important CYP isoforms, namely 2C9 and 3A4. The presented models are calibrated on 9122 drug-like compounds, using three different modelling approaches and two types of molecular description (classical molecular descriptors and binary fingerprints). For each isoform, three classification models are presented, based on a different approach and with different advantages: (1) a very simple and interpretable classification tree; (2) a local (k-Nearest Neighbor) model based classical descriptors and; (3) a model based on a recently proposed local classifier (N-Nearest Neighbor) on binary fingerprints. The salient features of the work are (1) the thorough model validation and the applicability domain assessment; (2) the descriptor interpretation, which highlighted the crucial aspects of P450-drug interaction; and (3) the consensus aggregation of models, which largely increased the prediction accuracy. Full article
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Open AccessArticle Development of an in Silico Model of DPPH• Free Radical Scavenging Capacity: Prediction of Antioxidant Activity of Coumarin Type Compounds
Int. J. Mol. Sci. 2016, 17(6), 881; doi:10.3390/ijms17060881
Received: 24 March 2016 / Revised: 20 May 2016 / Accepted: 25 May 2016 / Published: 7 June 2016
Cited by 3 | PDF Full-text (4200 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
A quantitative structure-activity relationship (QSAR) study of the 2,2-diphenyl-l-picrylhydrazyl (DPPH•) radical scavenging ability of 1373 chemical compounds, using DRAGON molecular descriptors (MD) and the neural network technique, a technique based on the multilayer multilayer perceptron (MLP), was developed. The built model demonstrated a
[...] Read more.
A quantitative structure-activity relationship (QSAR) study of the 2,2-diphenyl-l-picrylhydrazyl (DPPH•) radical scavenging ability of 1373 chemical compounds, using DRAGON molecular descriptors (MD) and the neural network technique, a technique based on the multilayer multilayer perceptron (MLP), was developed. The built model demonstrated a satisfactory performance for the training ( R 2 = 0.713 ) and test set ( Q ext 2 = 0.654 ) , respectively. To gain greater insight on the relevance of the MD contained in the MLP model, sensitivity and principal component analyses were performed. Moreover, structural and mechanistic interpretation was carried out to comprehend the relationship of the variables in the model with the modeled property. The constructed MLP model was employed to predict the radical scavenging ability for a group of coumarin-type compounds. Finally, in order to validate the model’s predictions, an in vitro assay for one of the compounds (4-hydroxycoumarin) was performed, showing a satisfactory proximity between the experimental and predicted pIC50 values. Full article
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Open AccessArticle Physico-Chemical and Structural Interpretation of Discrete Derivative Indices on N-Tuples Atoms
Int. J. Mol. Sci. 2016, 17(6), 812; doi:10.3390/ijms17060812
Received: 31 January 2016 / Revised: 27 April 2016 / Accepted: 4 May 2016 / Published: 27 May 2016
Cited by 3 | PDF Full-text (15608 KB) | HTML Full-text | XML Full-text
Abstract
This report examines the interpretation of the Graph Derivative Indices (GDIs) from three different perspectives (i.e., in structural, steric and electronic terms). It is found that the individual vertex frequencies may be expressed in terms of the geometrical and electronic reactivity
[...] Read more.
This report examines the interpretation of the Graph Derivative Indices (GDIs) from three different perspectives (i.e., in structural, steric and electronic terms). It is found that the individual vertex frequencies may be expressed in terms of the geometrical and electronic reactivity of the atoms and bonds, respectively. On the other hand, it is demonstrated that the GDIs are sensitive to progressive structural modifications in terms of: size, ramifications, electronic richness, conjugation effects and molecular symmetry. Moreover, it is observed that the GDIs quantify the interaction capacity among molecules and codify information on the activation entropy. A structure property relationship study reveals that there exists a direct correspondence between the individual frequencies of atoms and Hückel’s Free Valence, as well as between the atomic GDIs and the chemical shift in NMR, which collectively validates the theory that these indices codify steric and electronic information of the atoms in a molecule. Taking in consideration the regularity and coherence found in experiments performed with the GDIs, it is possible to say that GDIs possess plausible interpretation in structural and physicochemical terms. Full article
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Open AccessArticle Fast Modeling of Binding Affinities by Means of Superposing Significant Interaction Rules (SSIR) Method
Int. J. Mol. Sci. 2016, 17(6), 827; doi:10.3390/ijms17060827
Received: 8 March 2016 / Revised: 13 May 2016 / Accepted: 20 May 2016 / Published: 26 May 2016
Cited by 2 | PDF Full-text (1247 KB) | HTML Full-text | XML Full-text
Abstract
The Superposing Significant Interaction Rules (SSIR) method is described. It is a general combinatorial and symbolic procedure able to rank compounds belonging to combinatorial analogue series. The procedure generates structure-activity relationship (SAR) models and also serves as an inverse SAR tool. The method
[...] Read more.
The Superposing Significant Interaction Rules (SSIR) method is described. It is a general combinatorial and symbolic procedure able to rank compounds belonging to combinatorial analogue series. The procedure generates structure-activity relationship (SAR) models and also serves as an inverse SAR tool. The method is fast and can deal with large databases. SSIR operates from statistical significances calculated from the available library of compounds and according to the previously attached molecular labels of interest or non-interest. The required symbolic codification allows dealing with almost any combinatorial data set, even in a confidential manner, if desired. The application example categorizes molecules as binding or non-binding, and consensus ranking SAR models are generated from training and two distinct cross-validation methods: leave-one-out and balanced leave-two-out (BL2O), the latter being suited for the treatment of binary properties. Full article
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Open AccessArticle 3D-QSAR Studies on Barbituric Acid Derivatives as Urease Inhibitors and the Effect of Charges on the Quality of a Model
Int. J. Mol. Sci. 2016, 17(5), 657; doi:10.3390/ijms17050657
Received: 3 March 2016 / Revised: 5 April 2016 / Accepted: 26 April 2016 / Published: 30 April 2016
Cited by 2 | PDF Full-text (3156 KB) | HTML Full-text | XML Full-text
Abstract
Urease enzyme (EC 3.5.1.5) has been determined as a virulence factor in pathogenic microorganisms that are accountable for the development of different diseases in humans and animals. In continuance of our earlier study on the helicobacter pylori urease inhibition by barbituric acid derivatives,
[...] Read more.
Urease enzyme (EC 3.5.1.5) has been determined as a virulence factor in pathogenic microorganisms that are accountable for the development of different diseases in humans and animals. In continuance of our earlier study on the helicobacter pylori urease inhibition by barbituric acid derivatives, 3D-QSAR (three dimensional quantitative structural activity relationship) advance studies were performed by Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) methods. Different partial charges were calculated to examine their consequences on the predictive ability of the developed models. The finest developed model for CoMFA and CoMSIA were achieved by using MMFF94 charges. The developed CoMFA model gives significant results with cross-validation (q2) value of 0.597 and correlation coefficients (r2) of 0.897. Moreover, five different fields i.e., steric, electrostatic, and hydrophobic, H-bond acceptor and H-bond donors were used to produce a CoMSIA model, with q2 and r2 of 0.602 and 0.98, respectively. The generated models were further validated by using an external test set. Both models display good predictive power with r2pred ≥ 0.8. The analysis of obtained CoMFA and CoMSIA contour maps provided detailed insight for the promising modification of the barbituric acid derivatives with an enhanced biological activity. Full article
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Open AccessArticle Is It Reliable to Use Common Molecular Docking Methods for Comparing the Binding Affinities of Enantiomer Pairs for Their Protein Target?
Int. J. Mol. Sci. 2016, 17(4), 525; doi:10.3390/ijms17040525
Received: 22 February 2016 / Revised: 22 March 2016 / Accepted: 1 April 2016 / Published: 20 April 2016
Cited by 11 | PDF Full-text (412 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Molecular docking is a computational chemistry method which has become essential for the rational drug design process. In this context, it has had great impact as a successful tool for the study of ligand–receptor interaction modes, and for the exploration of large chemical
[...] Read more.
Molecular docking is a computational chemistry method which has become essential for the rational drug design process. In this context, it has had great impact as a successful tool for the study of ligand–receptor interaction modes, and for the exploration of large chemical datasets through virtual screening experiments. Despite their unquestionable merits, docking methods are not reliable for predicting binding energies due to the simple scoring functions they use. However, comparisons between two or three complexes using the predicted binding energies as a criterion are commonly found in the literature. In the present work we tested how wise is it to trust the docking energies when two complexes between a target protein and enantiomer pairs are compared. For this purpose, a ligand library composed by 141 enantiomeric pairs was used, including compounds with biological activities reported against seven protein targets. Docking results using the software Glide (considering extra precision (XP), standard precision (SP), and high-throughput virtual screening (HTVS) modes) and AutoDock Vina were compared with the reported biological activities using a classification scheme. Our test failed for all modes and targets, demonstrating that an accurate prediction when binding energies of enantiomers are compared using docking may be due to chance. We also compared pairs of compounds with different molecular weights and found the same results. Full article
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Open AccessArticle Structural Investigation for Optimization of Anthranilic Acid Derivatives as Partial FXR Agonists by in Silico Approaches
Int. J. Mol. Sci. 2016, 17(4), 536; doi:10.3390/ijms17040536
Received: 9 March 2016 / Revised: 29 March 2016 / Accepted: 5 April 2016 / Published: 8 April 2016
Cited by 2 | PDF Full-text (2231 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, a three level in silico approach was applied to investigate some important structural and physicochemical aspects of a series of anthranilic acid derivatives (AAD) newly identified as potent partial farnesoid X receptor (FXR) agonists. Initially, both two and three-dimensional quantitative
[...] Read more.
In this paper, a three level in silico approach was applied to investigate some important structural and physicochemical aspects of a series of anthranilic acid derivatives (AAD) newly identified as potent partial farnesoid X receptor (FXR) agonists. Initially, both two and three-dimensional quantitative structure activity relationship (2D- and 3D-QSAR) studies were performed based on such AAD by a stepwise technology combined with multiple linear regression and comparative molecular field analysis. The obtained 2D-QSAR model gave a high predictive ability (R2train = 0.935, R2test = 0.902, Q2LOO = 0.899). It also uncovered that number of rotatable single bonds (b_rotN), relative negative partial charges (RPC), oprea's lead-like (opr_leadlike), subdivided van der Waal’s surface area (SlogP_VSA2) and accessible surface area (ASA) were important features in defining activity. Additionally, the derived3D-QSAR model presented a higher predictive ability (R2train = 0.944, R2test = 0.892, Q2LOO = 0.802). Meanwhile, the derived contour maps from the 3D-QSAR model revealed the significant structural features (steric and electronic effects) required for improving FXR agonist activity. Finally, nine newly designed AAD with higher predicted EC50 values than the known template compound were docked into the FXR active site. The excellent molecular binding patterns of these molecules also suggested that they can be robust and potent partial FXR agonists in agreement with the QSAR results. Overall, these derived models may help to identify and design novel AAD with better FXR agonist activity. Full article
Open AccessArticle Hyaluronidase Inhibitory Activity of Pentacylic Triterpenoids from Prismatomeris tetrandra (Roxb.) K. Schum: Isolation, Synthesis and QSAR Study
Int. J. Mol. Sci. 2016, 17(2), 143; doi:10.3390/ijms17020143
Received: 21 December 2015 / Revised: 12 January 2016 / Accepted: 15 January 2016 / Published: 14 February 2016
Cited by 5 | PDF Full-text (1608 KB) | HTML Full-text | XML Full-text
Abstract
The mammalian hyaluronidase degrades hyaluronic acid by the cleavage of the β-1,4-glycosidic bond furnishing a tetrasaccharide molecule as the main product which is a highly angiogenic and potent inducer of inflammatory cytokines. Ursolic acid 1, isolated from Prismatomeris tetrandra, was identified
[...] Read more.
The mammalian hyaluronidase degrades hyaluronic acid by the cleavage of the β-1,4-glycosidic bond furnishing a tetrasaccharide molecule as the main product which is a highly angiogenic and potent inducer of inflammatory cytokines. Ursolic acid 1, isolated from Prismatomeris tetrandra, was identified as having the potential to develop inhibitors of hyaluronidase. A series of ursolic acid analogues were either synthesized via structure modification of ursolic acid 1 or commercially obtained. The evaluation of the inhibitory activity of these compounds on the hyaluronidase enzyme was conducted. Several structural, topological and quantum chemical descriptors for these compounds were calculated using semi empirical quantum chemical methods. A quantitative structure activity relationship study (QSAR) was performed to correlate these descriptors with the hyaluronidase inhibitory activity. The statistical characteristics provided by the best multi linear model (BML) (R2 = 0.9717, R2cv = 0.9506) indicated satisfactory stability and predictive ability of the developed model. The in silico molecular docking study which was used to determine the binding interactions revealed that the ursolic acid analog 22 had a strong affinity towards human hyaluronidase. Full article
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Open AccessArticle Computational Analysis of Structure-Based Interactions for Novel H1-Antihistamines
Int. J. Mol. Sci. 2016, 17(1), 129; doi:10.3390/ijms17010129
Received: 15 December 2015 / Revised: 5 January 2016 / Accepted: 13 January 2016 / Published: 19 January 2016
Cited by 5 | PDF Full-text (6385 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
As a chronic disorder, insomnia affects approximately 10% of the population at some time during their lives, and its treatment is often challenging. Since the antagonists of the H1 receptor, a protein prevalent in human central nervous system, have been proven as
[...] Read more.
As a chronic disorder, insomnia affects approximately 10% of the population at some time during their lives, and its treatment is often challenging. Since the antagonists of the H1 receptor, a protein prevalent in human central nervous system, have been proven as effective therapeutic agents for treating insomnia, the H1 receptor is quite possibly a promising target for developing potent anti-insomnia drugs. For the purpose of understanding the structural actors affecting the antagonism potency, presently a theoretical research of molecular interactions between 129 molecules and the H1 receptor is performed through three-dimensional quantitative structure-activity relationship (3D-QSAR) techniques. The ligand-based comparative molecular similarity indices analysis (CoMSIA) model (Q2 = 0.525, R2ncv = 0.891, R2pred = 0.807) has good quality for predicting the bioactivities of new chemicals. The cross-validated result suggests that the developed models have excellent internal and external predictability and consistency. The obtained contour maps were appraised for affinity trends for the investigated compounds, which provides significantly useful information in the rational drug design of novel anti-insomnia agents. Molecular docking was also performed to investigate the mode of interaction between the ligand and the active site of the receptor. Furthermore, as a supplementary tool to study the docking conformation of the antagonists in the H1 receptor binding pocket, molecular dynamics simulation was also applied, providing insights into the changes in the structure. All of the models and the derived information would, we hope, be of help for developing novel potent histamine H1 receptor antagonists, as well as exploring the H1-antihistamines interaction mechanism. Full article
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Review

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Open AccessReview Virtual Screening Approaches towards the Discovery of Toll-Like Receptor Modulators
Int. J. Mol. Sci. 2016, 17(9), 1508; doi:10.3390/ijms17091508
Received: 13 May 2016 / Revised: 1 July 2016 / Accepted: 22 August 2016 / Published: 9 September 2016
Cited by 5 | PDF Full-text (14758 KB) | HTML Full-text | XML Full-text
Abstract
This review aims to summarize the latest efforts performed in the search for novel chemical entities such as Toll-like receptor (TLR) modulators by means of virtual screening techniques. This is an emergent research field with only very recent (and successful) contributions. Identification of
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This review aims to summarize the latest efforts performed in the search for novel chemical entities such as Toll-like receptor (TLR) modulators by means of virtual screening techniques. This is an emergent research field with only very recent (and successful) contributions. Identification of drug-like molecules with potential therapeutic applications for the treatment of a variety of TLR-regulated diseases has attracted considerable interest due to the clinical potential. Additionally, the virtual screening databases and computational tools employed have been overviewed in a descriptive way, widening the scope for researchers interested in the field. Full article
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Open AccessReview Deep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications
Int. J. Mol. Sci. 2016, 17(8), 1313; doi:10.3390/ijms17081313
Received: 16 May 2016 / Revised: 14 July 2016 / Accepted: 25 July 2016 / Published: 11 August 2016
Cited by 9 | PDF Full-text (4536 KB) | HTML Full-text | XML Full-text
Abstract
Over the past decade, Deep Artificial Neural Networks (DNNs) have become the state-of-the-art algorithms in Machine Learning (ML), speech recognition, computer vision, natural language processing and many other tasks. This was made possible by the advancement in Big Data, Deep Learning (DL) and
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Over the past decade, Deep Artificial Neural Networks (DNNs) have become the state-of-the-art algorithms in Machine Learning (ML), speech recognition, computer vision, natural language processing and many other tasks. This was made possible by the advancement in Big Data, Deep Learning (DL) and drastically increased chip processing abilities, especially general-purpose graphical processing units (GPGPUs). All this has created a growing interest in making the most of the potential offered by DNNs in almost every field. An overview of the main architectures of DNNs, and their usefulness in Pharmacology and Bioinformatics are presented in this work. The featured applications are: drug design, virtual screening (VS), Quantitative Structure–Activity Relationship (QSAR) research, protein structure prediction and genomics (and other omics) data mining. The future need of neuromorphic hardware for DNNs is also discussed, and the two most advanced chips are reviewed: IBM TrueNorth and SpiNNaker. In addition, this review points out the importance of considering not only neurons, as DNNs and neuromorphic chips should also include glial cells, given the proven importance of astrocytes, a type of glial cell which contributes to information processing in the brain. The Deep Artificial Neuron–Astrocyte Networks (DANAN) could overcome the difficulties in architecture design, learning process and scalability of the current ML methods. Full article
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Open AccessReview A Brief Review of Computer-Assisted Approaches to Rational Design of Peptide Vaccines
Int. J. Mol. Sci. 2016, 17(5), 666; doi:10.3390/ijms17050666
Received: 1 April 2016 / Revised: 25 April 2016 / Accepted: 27 April 2016 / Published: 4 May 2016
Cited by 5 | PDF Full-text (363 KB) | HTML Full-text | XML Full-text
Abstract
The growing incidences of new viral diseases and increasingly frequent viral epidemics have strained therapeutic and preventive measures; the high mutability of viral genes puts additional strains on developmental efforts. Given the high cost and time requirements for new drugs development, vaccines remain
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The growing incidences of new viral diseases and increasingly frequent viral epidemics have strained therapeutic and preventive measures; the high mutability of viral genes puts additional strains on developmental efforts. Given the high cost and time requirements for new drugs development, vaccines remain as a viable alternative, but there too traditional techniques of live-attenuated or inactivated vaccines have the danger of allergenic reactions and others. Peptide vaccines have, over the last several years, begun to be looked on as more appropriate alternatives, which are economically affordable, require less time for development and hold the promise of multi-valent dosages. The developments in bioinformatics, proteomics, immunogenomics, structural biology and other sciences have spurred the growth of vaccinomics where computer assisted approaches serve to identify suitable peptide targets for eventual development of vaccines. In this mini-review we give a brief overview of some of the recent trends in computer assisted vaccine development with emphasis on the primary selection procedures of probable peptide candidates for vaccine development. Full article
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