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Special Issue "From Computational Chemistry to Complex Networks"

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A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Theoretical Chemistry".

Deadline for manuscript submissions: closed (15 December 2010)

Special Issue Editor

Guest Editor
Dr. Humberto González Díaz (Website)

Bioinformatics Section, Bioinformatics & Complex Network Analysis, Department of Microbiology & Parasitology, Faculty of Pharmacy, University of Santiago de Compostela (USC), Santiago de Compostela, 15782, Spain
Interests: drug design; QSAR; bioinformatics and complex network analysis

Special Issue Information

Dear Colleagues,

Many authors have been used Computational Chemistry (CC) methods to deal with structure-property relationships of molecules. Traditionally the field covered mainly Quantum Chemistry techniques (QC). Specifically, we have to mention the very important Ab initio methods and semi-empirical approaches as well. However, with the advent of pharmaceutical industry, chemistry of materials and biosciences CC has to fulfill different practical necessities. These necessities are related to the calculation of large databases of drugs, handling large structures, and/or approach to Bio-systems. Polymeric chains or complex structures in Nano Sciences like Buckminster Fullerenes, Nano-tubes, Dendrimers and Cyclodextrins are examples of large structures that CC have learn to handle. Large structures in the field of biosciences are proteins, protein-drug complexes, protein-protein complexes, RNA, DNA, branched Carbohydrates, Lipid membranes, etc.
One way is the assemble of more powerful computers, computing centers with remote web access for users and/or the development of parallel, in cloud, and/or distributed strategies like PC clusters or connecting personal consoles. In other direction, CC has seen the instruction of new approaches to improve and/or complements classic QC somehow. Researchers have developed Functional Density (FD) theories to make more tractable classic methods as well as Atoms In Molecules (AIM) and related approaches. Another direction was the implementation of Molecular Mechanics (MM) aided by Molecular Dynamics (MD) algorithms and the Monte Carlo (MC) methods. The combination of these approaches help to develop, for instance, drug-target Docking approaches and dynamic studies of proteins.
A last, simpler, but faster approach to CC is the implementation of Graph theory base methods to deal with very large databases or giant structures. Many CC authors have used graphs to represent the structure of drugs, xenobiotic substances, hazardous compounds, metabolites, materials and reagents. Calculating numerical parameters of these graphs called Topological Indices (TIs) or Connectivity indices (CIs) is a fast track way to describe molecules. We can use TIs combined with QC, or MM parameters to seek Quantitative Structure-Activity/Property Relationships (QSAR/QSPR) models. QSAR/QSPR models are equations that use structural parameters as input to predict the properties of the molecular system. The use of graphs is other gateway for CC to touch Bioinformatics because these graphs are essentially, in mathematical terms, the same objects used to study Protein Structure Networks, Protein Interaction Networks (PINs), Proteome, Metabolic pathway networks (Metabolome), drug-target and drug-gene-disease (Diseasome) and other complex systems hard to handle with classic QC, MM, MD and Docking methods. In this sense, we invite all colleagues to submit manuscripts to this special issue reviewing all these aspects in order to give a more complete picture of modern CC. At follows we give list of topics covered by this special issue as well as some guideline references to help potential authors to fit on this number. We welcome your submissions.

The issue includes but is not limited to the following topics:

  • Quantum Chemistry techniques (QC)
  • Functional Density (FD) theories
  • Atoms In Molecules (AIM) and related approaches
  • Molecular Mechanics (MM)
  • Molecular Dynamics (MD) algorithms
  • Drug-target, protein-protein and other Docking methods
  • Monte Carlo (MC) methods
  • Quantitative Structure-Activity/Property Relationships (QSAR/QSPR) models
  • Graph theory and Complex Networks
  • Topological Indices (TIs) or Connectivity indices (CIs)
  • Protein Structure Networks, Protein Interaction Networks (PINs) and Proteome

Some Guideline references:

[1] Todeschini R, Consonni V. Handbook of Molecular Descriptors: Wiley-VCH 2002.
[2] Bornholdt S, Schuster HG. Handbook of Graphs and Complex Networks: From the Genome to the Internet. Wheinheim: WILEY-VCH GmbH & CO. KGa. 2003.
[3] González-Díaz H, al. e. Topological Indices for Medicinal Chemistry, Biology, Parasitology, Neurological and Social Networks. Hardcover ed. Kerala: Transworld Research Network 2010.
[4] González-Díaz H, Duardo-Sanchez A, Ubeira FM, Prado-Prado F, Pérez-Montoto LG, Concu R, et al. Review of MARCH-INSIDE & Complex Networks prediction of Drugs: ADMET, Anti-parasite Activity, Metabolizing Enzymes and Cardiotoxicity Proteome Biomarkers Curr Drug Metab. 2010;11(4):doi: 1389-2002/10.
[5] González-Díaz H, Prado-Prado F, Pérez-Montoto LG, Duardo-Sánchez A, López-Díaz A. QSAR Models for Proteins of Parasitic Organisms, Plants and Human Guests: Theory, Applications, Legal Protection, Taxes, and Regulatory Issues. Curr Proteomics. 2009;6:214-27.
[6] González-Díaz H, González-Díaz Y, Santana L, Ubeira FM, Uriarte E. Proteomics, networks and connectivity indices. Proteomics. 2008;8:750-78.
[7] Robinson AL. Computational Chemistry: Getting More from a Minicomputer. Science (New York, NY. 1976 Aug 6;193(4252):470-2.
[8] Khedkar SA. Current computational approaches in medicinal chemistry. Current topics in medicinal chemistry.10(1):1-2.
[9] Streitwieser A. Perspectives on computational organic chemistry. The Journal of organic chemistry. 2009 Jun 19;74(12):4433-46.
[10] Villar HO. Computational medicinal chemistry. Current topics in medicinal chemistry. 2007;7(15):1489-90.
[11] Kaltsoyannis N. Recent developments in computational actinide chemistry. Chemical Society reviews. 2003 Jan;32(1):9-16.
[12] Lipkowitz KB. Applications of Computational Chemistry to the Study of Cyclodextrins. Chemical reviews. 1998 Jul 30;98(5):1829-74.
[13] Davidson ER. Computational transition metal chemistry. Chemical reviews. 2000 Feb 9;100(2):351-2.
[14] Bures MG, Martin YC. Computational methods in molecular diversity and combinatorial chemistry. Current opinion in chemical biology. 1998 Jun;2(3):376-80.
[15] Woods RJ. Computational carbohydrate chemistry: what theoretical methods can tell us. Glycoconjugate journal. 1998 Mar;15(3):209-16.
[16] Bohacek RS, McMartin C. Modern computational chemistry and drug discovery: structure generating programs. Current opinion in chemical biology. 1997 Aug;1(2):157-61.

Dr. Humberto González Díaz
Guest Editor

Published Papers (6 papers)

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Research

Open AccessArticle Computational Aspects of Carbon and Boron Nanotubes
Molecules 2010, 15(12), 8709-8722; doi:10.3390/molecules15128709
Received: 19 September 2010 / Revised: 16 November 2010 / Accepted: 17 November 2010 / Published: 30 November 2010
Cited by 7 | PDF Full-text (574 KB)
Abstract
Carbon hexagonal nanotubes, boron triangular nanotubes and boron a-nanotubes are a few popular nano structures. Computational researchers look at these structures as graphs where each atom is a node and an atomic bond is an edge. While researchers are discussing the differences [...] Read more.
Carbon hexagonal nanotubes, boron triangular nanotubes and boron a-nanotubes are a few popular nano structures. Computational researchers look at these structures as graphs where each atom is a node and an atomic bond is an edge. While researchers are discussing the differences among the three nanotubes, we identify the topological and structural similarities among them. We show that the three nanotubes have the same maximum independent set and their matching ratios are independent of the number of columns. In addition, we illustrate that they also have similar underlying broadcasting spanning tree and identical communication behavior. Full article
(This article belongs to the Special Issue From Computational Chemistry to Complex Networks)
Open AccessArticle Towards the Development of Synthetic Routes Using Theoretical Calculations: An Application of In Silico Screening to 2,6-Dimethylchroman-4-one
Molecules 2010, 15(11), 8289-8304; doi:10.3390/molecules15118289
Received: 17 September 2010 / Revised: 9 November 2010 / Accepted: 11 November 2010 / Published: 15 November 2010
Cited by 4 | PDF Full-text (327 KB)
Abstract
This study describes an attempt to develop a synthetic route using theoretical calculations, i.e., in silico synthesis route development. The KOSP program created four potential synthetic routes for generating 2,6-dimethylchroman-4-one. In silico screening of these four synthetic routes was then performed. [...] Read more.
This study describes an attempt to develop a synthetic route using theoretical calculations, i.e., in silico synthesis route development. The KOSP program created four potential synthetic routes for generating 2,6-dimethylchroman-4-one. In silico screening of these four synthetic routes was then performed. In silico screening involves theoretical analysis of synthetic routes prior to actual experimental work. A synthetic route using the Mitsunobu reaction had already been reported by Hoddgets et al. Theoretical investigations were also conducted on two SNAr reactions as well as a Michael reaction before they were examined experimentally. In silico screening using DFT calculations indicated that only the Michael reaction was likely to produce the target. Experimental work confirmed that the target was obtained in a yield of 76.4% using the Michael reaction. The other two routes, except for the Mitsunobu reaction, failed to generate the target. Our results demonstrate that theoretical calculations can be used to narrow down the number of experiments that need to be conducted when developing novel synthetic routes. Full article
(This article belongs to the Special Issue From Computational Chemistry to Complex Networks)
Figures

Open AccessArticle Analysis of Protein Pathway Networks Using Hybrid Properties
Molecules 2010, 15(11), 8177-8192; doi:10.3390/molecules15118177
Received: 5 August 2010 / Revised: 11 November 2010 / Accepted: 12 November 2010 / Published: 12 November 2010
Cited by 43 | PDF Full-text (188 KB) | Supplementary Files
Abstract
Given a protein-forming system, i.e., a system consisting of certain number of different proteins, can it form a biologically meaningful pathway? This is a fundamental problem in systems biology and proteomics. During the past decade, a vast amount of information on [...] Read more.
Given a protein-forming system, i.e., a system consisting of certain number of different proteins, can it form a biologically meaningful pathway? This is a fundamental problem in systems biology and proteomics. During the past decade, a vast amount of information on different organisms, at both the genetic and metabolic levels, has been accumulated and systematically stored in various specific databases, such as KEGG, ENZYME, BRENDA, EcoCyc and MetaCyc. These data have made it feasible to address such an essential problem. In this paper, we have analyzed known regulatory pathways in humans by extracting different (biological and graphic) features from each of the 17,069 protein-formed systems, of which 169 are positive pathways, i.e., known regulatory pathways taken from KEGG; while 16,900 were negative, i.e., not formed as a biologically meaningful pathway. Each of these protein-forming systems was represented by 352 features, of which 88 are graph features and 264 biological features. To analyze these features, the “Minimum Redundancy Maximum Relevance” and the “Incremental Feature Selection” techniques were utilized to select a set of 22 optimal features to query whether a protein-forming system is able to form a biologically meaningful pathway or not. It was found through cross-validation that the overall success rate thus obtained in identifying the positive pathways was 79.88%. It is anticipated that, this novel approach and encouraging result, although preliminary yet, may stimulate extensive investigations into this important topic. Full article
(This article belongs to the Special Issue From Computational Chemistry to Complex Networks)
Open AccessArticle Using Topological Indices to Predict Anti-Alzheimer and Anti-Parasitic GSK-3 Inhibitors by Multi-Target QSAR in Silico Screening
Molecules 2010, 15(8), 5408-5422; doi:10.3390/molecules15085408
Received: 8 June 2010 / Revised: 27 July 2010 / Accepted: 2 August 2010 / Published: 9 August 2010
Cited by 9 | PDF Full-text (330 KB)
Abstract
Plasmodium falciparum, Leishmania, Trypanosomes, are the causers of diseases such as malaria, leishmaniasis and African trypanosomiasis that nowadays are the most serious parasitic health problems worldwide. The great number of deaths and the few drugs available against these parasites, [...] Read more.
Plasmodium falciparum, Leishmania, Trypanosomes, are the causers of diseases such as malaria, leishmaniasis and African trypanosomiasis that nowadays are the most serious parasitic health problems worldwide. The great number of deaths and the few drugs available against these parasites, make necessary the search for new drugs. Some of these antiparasitic drugs also are GSK-3 inhibitors. GSKI-3 are candidates to develop drugs for the treatment of Alzheimer’s disease. In this work topological descriptors for a large series of 3,370 active/non-active compounds were initially calculated with the ModesLab software. Linear Discriminant Analysis was used to fit the classification function and it predicts heterogeneous series of compounds like paullones, indirubins, meridians, etc. This study thus provided a general evaluation of these types of molecules. Full article
(This article belongs to the Special Issue From Computational Chemistry to Complex Networks)
Figures

Open AccessArticle Effects of Time Point Measurement on the Reconstruction of Gene Regulatory Networks
Molecules 2010, 15(8), 5354-5368; doi:10.3390/molecules15085354
Received: 5 July 2010 / Revised: 30 July 2010 / Accepted: 2 August 2010 / Published: 4 August 2010
Cited by 11 | PDF Full-text (595 KB)
Abstract
With the availability of high-throughput gene expression data in the post-genomic era, reconstruction of gene regulatory networks has become a hot topic. Regulatory networks have been intensively studied over the last decade and many software tools are currently available. However, the impact [...] Read more.
With the availability of high-throughput gene expression data in the post-genomic era, reconstruction of gene regulatory networks has become a hot topic. Regulatory networks have been intensively studied over the last decade and many software tools are currently available. However, the impact of time point selection on network reconstruction is often underestimated. In this paper we apply the Dynamic Bayesian network (DBN) to construct the Arabidopsis gene regulatory networks by analyzing the time-series gene microarray data. In order to evaluate the impact of time point measurement on network reconstruction, we deleted time points one by one to yield 11 distinct groups of incomplete time series. Then the gene regulatory networks constructed based on complete and incomplete data series are compared in terms of statistics at different levels. Two time points are found to play a significant role in the Arabidopsis gene regulatory networks. Pathway analysis of significant nodes revealed three key regulatory genes. In addition, important regulations between genes, which were insensitive to the time point measurement, were also identified. Full article
(This article belongs to the Special Issue From Computational Chemistry to Complex Networks)
Figures

Open AccessArticle Machine Learning Techniques for Single Nucleotide Polymorphism—Disease Classification Models in Schizophrenia
Molecules 2010, 15(7), 4875-4889; doi:10.3390/molecules15074875
Received: 4 June 2010 / Revised: 8 July 2010 / Accepted: 9 July 2010 / Published: 12 July 2010
Cited by 10 | PDF Full-text (614 KB)
Abstract
Single nucleotide polymorphisms (SNPs) can be used as inputs in disease computational studies such as pattern searching and classification models. Schizophrenia is an example of a complex disease with an important social impact. The multiple causes of this disease create the need [...] Read more.
Single nucleotide polymorphisms (SNPs) can be used as inputs in disease computational studies such as pattern searching and classification models. Schizophrenia is an example of a complex disease with an important social impact. The multiple causes of this disease create the need of new genetic or proteomic patterns that can diagnose patients using biological information. This work presents a computational study of disease machine learning classification models using only single nucleotide polymorphisms at the HTR2A and DRD3 genes from Galician (Northwest Spain) schizophrenic patients. These classification models establish for the first time, to the best knowledge of the authors, a relationship between the sequence of the nucleic acid molecule and schizophrenia (Quantitative Genotype – Disease Relationships) that can automatically recognize schizophrenia DNA sequences and correctly classify between 78.3–93.8% of schizophrenia subjects when using datasets which include simulated negative subjects and a linear artificial neural network. Full article
(This article belongs to the Special Issue From Computational Chemistry to Complex Networks)

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