International Journal of Chem-informatics Research vol 2 issue 1 by JournalsPub

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International Journal of Cheminformatics Research International Journal of Cheminformatics Research aims to serve as a platform for the propagation of innovative ideas and research in entire areas of Cheminformatics. All manuscripts go through a rigorous peer review process. It is designed to create interest among researchers in this field which deal with the use of computer and informational techniques applied to wider range problems in the field of chemistry. The main function of this journal is in the areas of topology, chemical graph theory, information retrieval and data mining in the chemical field. Cheminformatics can also be used in chemical and allied industries in several other forms. Journal aims to publish original, high quality papers that are peer-reviewed by our expert editorial team to ensure the publication of only good quality papers.

Focus and Scope of the Journal !

Chemical databases

! Drug discovery ! Computer-assisted synthesis design ! Computer-assisted molecular design ! Organizing chemical facts into databases ! Molecular modeling ! Structure generators ! Modeling 3D structures ! Database mining ! Graph mining ! Molecule mining ! Sequence mining ! Digital libraries

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PUBLICATION MANAGEMENT TEAM

Chairman Mr. Puneet Mehrotra Managing Director, JournalsPub, New Delhi

INTERNAL MEMBERS

Associate Manager Hidam Renubala

Commissioning Editors Ankita Singh

Priyanka Garg

Akanksha Marwah

Chhavi Goel

Deepika Bhadauria

Shrawani Verma

EDITORIAL BOARD MEMBERS Dr. Yogesh Kumar Sharma Swami Shraddhanand College, Department of Chemistry, University of Delhi, New Delhi, India

Dr. Monika Gupta Department of Chemistry, University of Jammu, Jammu, India

Dr. P. V. Singh Department of Chemistry, Institute of Technology & Management, Uttar Pradesh, India

Dr. Sandeep Rai Department of Chemistry/Polymer and Rubber, Shroff S.R. Rotary Institute of Chemical Technology, Gujarat, India

Dr. Kishore Dasari Navin Fluorine International Limited, India

Dr. Kaman Singh Department of Chemistry (DST-FIST-PURSE Sponsored) Centre of Excellence (U.P. Govt., UGC-DRS (I) â&#x20AC;&#x201C;SAP Sponsored Department) Faculty of Science, University of Lucknow, Lucknow, India

Dr. Ajit Manharlal Bhavsar Dishman Pharmaceuticals and Chemicals Limited, Ahmedabad, India

Dr. Satyabrata Mohapatra Emami Limited, India

Dr. Susarla Venkata Ananta Rama Sastry MVGR College of Engineering, Vizianagaram, Andhra Pradesh, India

From the Editor's Desk Dear Readers, We would like to present, with great pleasure, the inaugural volume of a new scholarly journal, International Journal of Cheminformatics Research. This journal is part of the Applied Sciences, and is devoted to the scope of present chemical synthesis and chemical reactions issues, from theoretical aspects to application-dependent studies and the validation of emerging technologies. This new journal was planned and established to represent the growing needs of Cheminformatics Research as an emerging and increasingly vital field, now widely recognized as an integral part of scientific and technical investigations. Its mission is to become a voice of the Cheminformatics Research Science community, addressing researchers and practitioners in this area. The core vision of International Journal of Cheminformatics Research in Journals Pub is to propagate novel awareness and know-how for the profit of mankind ranging from the academic and professional research societies to industry practitioners in a range of topics in Chem-informatics Research in general. Journals Pub acts as a pathfinder for the scientific community to published their papers at excellently, well-time & successfully. International Journal of Cheminformatics Research focuses on original high-quality research in the realm of Chemical databases, Drug discovery, Computer-assisted synthesis design, Computer-assisted molecular design, Structure generators, Modeling 3D structures, Database mining, Digital libraries and many more. Many scientists and researchers have contributed to the creation and the success of the Cheminformatics Research community. We are very thankful to everybody within that community who supported the idea of creating an innovative platform. We are certain that this issue will be followed by many others, reporting new developments in the field of Cheminformatics Research. This issue would not have been possible without the great support of the Editorial Board members, and we would like to express our sincere thanks to all of them. We would also like to express our gratitude to the editorial staff of JournalsPub, who supported us at every stage of the project. It is our hope that this fine collection of articles will be a valuable resource for Cheminformatics Research readers and will stimulate further research into the vibrant area of Cheminformatics Research. Puneet Mehrotra Managing Director

Contents 1. Calculation of pKa Values of Alkanolamines â&#x20AC;&#x201C; A DFT-B3LYP Computational Analysis V.K. Rajan, K. Muraleedharan

2. An Introduction to Homology Modeling Bharat Singh

3. Molecular Modeling: An Overview Sona Chahal

4. Chemical Database and Its Types: Brief Review Bharat Singh

5. Data Mining and Drug Discovery Shivani Sharma

International Journal of Cheminformatics Research Vol. 2: Issue 1

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Calculation of pKa Values of Alkanolamines – A DFT-B3LYP Computational Analysis V.K. Rajan, K. Muraleedharan* Department of Chemistry, University of Calicut, Malappuram, India

Abstract Greenhouse effect, an important environmental concern today, is particularly due to the emission of carbon dioxide. Removal of CO2 and its storage called carbon dioxide capture and storage (CCS) is thus a noble area in research. Many experimental works are done in this area to screen a suitable solvent for CCS. But the experimental measurements on pK a values, free energy values, enthalpy values, etc. are very difficult and have less accuracy. The present work employs a computational analysis to predict the pKa values of alkanolamines, a suitable candidate for CCS. The poly-functionality makes the alkanolamines as an industrially important molecule. A DFT-B3LYP level of theory was employed on different alkanolamines with 6-31+G (d, p) as basis set. Since most of the reactions starts with protonation or deprotonation reaction, the study of acid dissociation constants, i.e., the pKa values is very useful. The pKa values of alkanolamines are of importance that they play a major role in explaining the mechanism of carbon dioxide capture by them. Different alkanolamine molecules including primary, secondary, and tertiary are studied. The present method uses the free energy calculation to get the pKa values of alkanolmines. The linear relationship of the pKa value with the free energy of protonation is verified. Also computed the gas phase basicity and gas phase proton affinity of different alkanolamines. The structure of alkanolamin molecule has an effect on their pKa value and this determines the trend in their pKa values. They have pKa values less than that of corresponding alkylamines. The hydrogen bonding, sterric effect, etc. have major role in the pKa values of different alkanolamines. All the values are computed except the Gibb’s free energy (in gas phase and in solution phase) and the enthalpy values of proton (in gas phase), which is taken from the literature. The gas phase basicity and gas phase proton affinity are also varied with the structure of different alkanolamine molecules. The method employed is simple and the results are in good agreement with the experimental results. The study can further extended to the temperature dependence of pKa values, as the postcombustion of CCS has temperature dependence and to screen suitable candidates for carbon dioxide removal. Keywords: alkanolamines, density functional theory, gas phase basicity, pKa values, proton affinity

INTRODUCTION A large amount of anthropogenic greenhouse gases are being emitted to the atmosphere at an increased rate. This results in an increased atmospheric temperature and become a great threat to both human beings and the environment. Carbon dioxide (CO2), a major greenhouse

gas, partakes about 60% of global warming. This makes the removal of CO2 from gas streams and thereby reducing the atmospheric CO2 concentration, an

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International Journal of Cheminformatics Research Vol. 2: Issue 1

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An Introduction to Homology Modeling Bharat Singh Maharshi Dayanand University, Rohtak, Uttar Pradesh, India

‘Homology modeling’, also known as comparative modeling or template-based modeling (TBM) refers to modeling a protein three-dimensional structure using a known experimentally determined structure of a homologous protein as a template. Identifying the protein structure has been the ultimate goal in protein modeling studies. Knowing the protein structure assists in the study of protein function and dynamics, generating in silico protein models, structure-based drug design, interaction with other ligands, antigenic behavior and rational designing. Homology modeling gives the researchers and scientists the structural information of proteins in situations when experimental techniques fail. Many proteins are simply too large for NMR analysis and cannot be crystallized for X-ray diffraction. Also, it provides the molecular biologists and biochemists with ‘low-resolution’ structures, containing information about the spatial arrangement of important residues in the protein macromolecules. Such information helps in designing of new set of experiments. As an example, the design of site-directed mutagenesis experiments can be significantly improvised using low-resolution model structures. The method of homology modeling is based on two basic observations: 1. The structure of any protein molecule is uniquely determined by its amino acid sequence. So, once the sequence is known, it would be easier to obtain the protein structure. 2. Protein structures are believed to be conserved in nature. During evolution,

the structures are considered more stable and changes at a much slower rate than the associated sequences. This implies that similar sequences adapt identical structures while distantly related sequences fold into similar structures. Steps involved in homology modeling include: 1. Identification of the template. 2. Amino acid sequence alignment. 3. Alignment correction. 4. Generation of backbone. 5. Loop generation. 6. Generation and optimization of side chains. 7. ab initio loop building. 8. Overall model optimization. 9. Final verification of model, quality criteria and model quality. Once the template is selected, next step is to make a multiple sequence alignment which includes the desired sequence, sequence of the template and few other protein sequences belonging to the same family of proteins. This gives an overview of the general characteristics of that protein family including the degree of conservation, consensus sequence motifs, secondary structure prediction, etc.

After the sequence analysis is done, and the alignment is corrected, the next step is modeling the protein structure. Modeling softwares are available that will thread the desired sequence on the template structure and creates a preliminary model (the backbone) of the protein in question. Some of the servers for modeling are: Swiss

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Molecular Modeling: An Overview Sona Chahal Department of Electrical and Electronics, Lord Krishna College of Engineering, Ghaziabad, Uttar Pradesh, India

Molecular modeling has now become todayâ&#x20AC;&#x2122;s one of the hottest themes for drug discovery and design tools. It has become an integral part of determining, explaining, and predicting the properties of small organic compounds for potential drug candidates. It has become a valuable and essential tool to medicinal chemists for the drug designing process. Molecular modeling involves a range of computerized techniques based on theoretical chemistry methods and experimental data to predict and determine the molecular and biological properties. Molecular Modeling Tools The tools for molecular modeling have evolved considerably from physical models and calculators to computers as visualization aids, computers running commercially written analysis packages and most recently integration using internet based tools and work benches based on HTML, Java Script, etc. Now-a-days, the tools used for drug modeling using computers include: hardware and software components. Modeling Strategies Two most commonly used modeling strategies include: direct drug design and indirect drug design. Direct Drug Design In this direct approach, the 3-D features of the known receptor site are determined from X-ray crystallography and a lead molecule is designed. In direct design method, the receptor site geometry is already known, and the problem is to find

a molecule that fulfills some geometric constraints and is also a good chemical match. After finding ideal candidates according to these criteria, a docking step with energy minimization can be used to predict binding strength. Indirect Drug Design The indirect drug design approach involves comparative analysis of structural features of the already known active and inactive molecules that are complementary with a hypothetical receptor site. If the site geometry is not known, as is often the case, the designer must base the design on other ligand molecules that bind well to the site. Applications of Molecular Modeling The starting point for many computer assisted molecular modeling studies is generally a two-dimensional drawing of a required molecule. Then the twodimensional structures are transformed into three-dimensional representations to study chemical properties. Various applications of computer assisted molecular modeling techniques are reviewed here. Generation of Chemical Structures The 3D structures of molecules can be created by several common building functions including create-bond, breakbond, fuserings, delete-atom, add-atomhydrogens, invest chiral center, etc. Computer modeling allows chemists to build dynamic models of molecules or compounds which in turn allow them to visualize molecular geometry and demonstrate chemical principles.

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International Journal of Cheminformatics Research Vol. 2: Issue 1

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Chemical Database and Its Types: Brief Review Bharat Singh* Maharshi Dayanand University, Rohtak, Haryana, India

Abstract Chemical database is a powerful branch of cheminformatics research and development, the chief function of which is storing, indexing and searching of information related to chemical compounds and reaction synthesis using computer science and information technology. Computer-based representation of chemicals makes it possible to organize data in chemical databases-collections of chemical structures and associated properties. The typical kinds of information found in a chemical database are considered-identification, structural, and associated data. Here, in this brief review, an outline of the objective of chemical databases and their types is described.

INTRODUCTION Chemical databases are an important tool in the field of cheminformatics research and development. Cheminformatics is that discipline of science that uses the techniques of computer science and informational skills to address a range of problems in the field of chemistry.[1,2] The aim of cheminformatics is to develop novel techniques foe collection and manipulation of chemical information as well as to develop algorithms for discoveries and predictions from chemical databases. A chemical database is a database that is especially designed to store chemical information. The chemical information collected and stored in chemical databases can be in the form of a structure, spectra, chemical process or reaction, chemical synthesis, information on data analysis from analytical chemistry to make predictions on the quality, physical, chemical or biological properties of compounds, thermodynamical process, thermophysical, etc.[2-4] The use of such information technology has become an important part of cheminformatics research.

Types of Chemical Database Several types of chemical databases are available depending upon the particular type of chemical data stored in them. These include:  Chemical structure database: This type of chemical databases store chemical structures and constitutes molecular geometry, electronic structure and crystal structure of molecules. Such databases are capable to handle the storage and searching of information on millions of molecules. ChemSpider is an example of such type of database.  Literature database: A literature database stores the chemical data in context to structures and other chemical information in relation to relevant references such as those in academic papers or patents. Examples are the STN (Scientific and technical information network), Reaxys and Scifinder databases.[4,5]

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Data Mining and Drug Discovery Shivani Sharma* Department of Bio Pharma Sciences, C.G.C Gharuan, Mohali, Punjab, India

Abstract Today, drug discovery makes use of cheminformatics research to overcome the drawbacks and shortcomings of traditional drug discovery procedure. Cheminformatics utilizes computer information technology to solve and answer the wide range of queries and problems in the field of chemistry. With the aid of data mining, data is collected relating chemical structures to each other. This has proven beneficial in the area of drug discovery and designing. Here, in this short communication, the use of data mining in context to drug discovery and its step-by-step procedure is briefed.

INTRODUCTION Designing new drugs for targeting specific diseases requires extensive research and analysis of several bioactive molecules or chemical compounds that can be used as ideal candidates as drugs. One approach towards achieving this objective is to analyze a database of known and tested molecules in order to find structural properties that determine whether the molecule will be active or inactive, so that future chemical tests can be focused on the most promising candidates.[1] Data mining generates large numbers of related chemical compounds that can be used for drug designing.

For better understanding, let us first try to know the steps involved in drug discovery process.

Also, if the biological effects of different molecules are known one can combine them to make chemicals with desired biological outcomes.

Data Integration: At this stage, the entire multiple data sources are combined in a common source called the data warehouse. The data warehouse is the place where all the data is centralized. The data stored in the data warehouse possess certain characteristics viz. they should be timedependent, non-volatile, subject orientated and integrated.

The data mining technique, also called as ‘Clustering Process’, divides the databases of unknown drugs in clusters based on their similarity.[1,2] The clusters of unknown but similar drugs are assessed and compared with the clusters of some particular such as HIV drugs to discover from unknown drugs those drugs that are similar in properties with the already known and available drugs.

Data Pre-processing: This defines the initial pre-processing steps of the data. Data Selection: In this step, the data to be used is selected based on its previously known information and potential to yield desired product. The selected data sources are referred as data marts which are then used in discovery process. Results are highly dependent on the target dataset; therefore care should be taken in selecting the data.[2,3]

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« « « «

¬ ¬ International Journal of Industrial Engineering

Manufacturing International Journal of Mechanical Dynamics and Analysis International Journal of Fracture and damage Mechanics International Journal of Structural Mechanics and Finite Elements

and Design ¬ International Journal of Manufacturing and

Materials Processing ¬ International Journal of Mechanical Handling and

Automation

Technologies « International Journal of Digital Electronics « International Journal of Digital Communication and Analog

« International Journal of Renewable Energy and its

Commercialization « International Journal of Environmental Chemistry « International Journal of Agrochemistry « International Journal of Prevention and Control of Industrial

1 more...

Civil Engineering

Planning « International Journal of Architecture and Infrastructure

Planning « International Journal of Rural and Regional Planning

« « « «

Development

« International Journal of Town Planning and Management

2 more...

Electrical Engineering

« « « «

Nursing « « « « «

Material Sciences and Engineering

« « « «

5 more...

Biotechnology « International Journal of Industrial Biotechnology and « « « «

Jan – Jun 2016

Pollution

Architecture « International Journal of Housing and Human Settlement

« « « « «

IJCR

Chemical Engineering

5 more...

Signals

Cheminformatics Research

5 more...

Electronics and Telecommunication « International Journal of Radio Frequency Design « International Journal of VLSI Design and Technology « International Journal of Embedded Systems and Emerging

International Journal of

Nanotechnology « International Journal of Applied Nanotechnology « International Journal of Nanomaterials and Nanostructures « International Journals of Nanobiotechnology

Chemistry

Physics « International Journal of Solid State Materials « International Journal of Optical Sciences