Journal Menu► ▼ Journal Menu
Journal Browser► ▼ Journal Browser
Special Issue "From Computational Chemistry to Complex Networks"
Deadline for manuscript submissions: closed (15 December 2010).
2. IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Biscay, Spain
Interests: cheminformatics; bioinformatics; machine learning; complex networks; computational nanoscience
Special Issues and Collections in MDPI journals
Special Issue in Data: Big Data Analysis and QSAR/QSPR Research in Chemistry, Bio-Medical, and Network Sciences
Special Issue in Nanomaterials: Experimental Nanosciences, Computational Chemistry, and Data Analysis
Special Issue in Applied Sciences: Complex Networks and Machine Learning: From Molecular to Social Sciences
Special Issue in Molecules: Cheminformatics, Past, Present, and Future: From Chemistry to Nanotechnology and Complex Systems
Special Issue in Biomolecules: Big Data Analysis in Biomolecular Research, Bioinformatics, and Systems Biology with Complex Networks and Multi-Label Machine Learning Models
Special Issue in Nanomaterials: Computational Nanomaterials Design: Databases, Machine Learning, Artificial Intelligence, and Big Data Analysis
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:
 Todeschini R, Consonni V. Handbook of Molecular Descriptors: Wiley-VCH 2002.
 Bornholdt S, Schuster HG. Handbook of Graphs and Complex Networks: From the Genome to the Internet. Wheinheim: WILEY-VCH GmbH & CO. KGa. 2003.
 González-Díaz H, al. e. Topological Indices for Medicinal Chemistry, Biology, Parasitology, Neurological and Social Networks. Hardcover ed. Kerala: Transworld Research Network 2010.
 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.
 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.
 González-Díaz H, González-Díaz Y, Santana L, Ubeira FM, Uriarte E. Proteomics, networks and connectivity indices. Proteomics. 2008;8:750-78.
 Robinson AL. Computational Chemistry: Getting More from a Minicomputer. Science (New York, NY. 1976 Aug 6;193(4252):470-2.
 Khedkar SA. Current computational approaches in medicinal chemistry. Current topics in medicinal chemistry.10(1):1-2.
 Streitwieser A. Perspectives on computational organic chemistry. The Journal of organic chemistry. 2009 Jun 19;74(12):4433-46.
 Villar HO. Computational medicinal chemistry. Current topics in medicinal chemistry. 2007;7(15):1489-90.
 Kaltsoyannis N. Recent developments in computational actinide chemistry. Chemical Society reviews. 2003 Jan;32(1):9-16.
 Lipkowitz KB. Applications of Computational Chemistry to the Study of Cyclodextrins. Chemical reviews. 1998 Jul 30;98(5):1829-74.
 Davidson ER. Computational transition metal chemistry. Chemical reviews. 2000 Feb 9;100(2):351-2.
 Bures MG, Martin YC. Computational methods in molecular diversity and combinatorial chemistry. Current opinion in chemical biology. 1998 Jun;2(3):376-80.
 Woods RJ. Computational carbohydrate chemistry: what theoretical methods can tell us. Glycoconjugate journal. 1998 Mar;15(3):209-16.
 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