Top PDF A Study on Cheminformatics and its Applications on Modern Drug Discovery

A Study on Cheminformatics and its Applications on Modern Drug Discovery

A Study on Cheminformatics and its Applications on Modern Drug Discovery

Abstract Discovering drugs to a disease is still a challenging task for medical researchers due to the complex structures of biomolecules which are responsible for disease such as AIDS, Cancer, Autism, Alzimear etc. Design and development of new efficient anti-drugs for the disease without any side effects are becoming mandatory in the recent history of human life cycle due to changes in various factors which includes food habit, environmental and migration in human life style. Cheminformaticds deals with discovering drugs based in modern drug discovery techniques which in turn rectifies complex issues in traditional drug discovery system. Cheminformatics tools, helps medical chemist for better understanding of complex structures of chemical compounds. Cheminformatics is a new emerging interdisciplinary field which primarily aims to discover Novel Chemical Entities [NCE] which ultimately results in design of new molecule [chemical data]. It also plays an important role for collecting, storing and analysing the chemical data. This paper focuses on cheminformatics and its applications on drug discovery and modern drug discovery techniques which helps chemist and medical researchers for finding solution to the complex disease.
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Cheminformatics and its Role in the Modern Drug Discovery Process

Cheminformatics and its Role in the Modern Drug Discovery Process

 open, modular, platform and vendor independent architecture  integration with other scientific applications  more than 1’800 registered users  used from all Novartis research sites (Tsukuba, Wien, Basel, Horsham, Cambridge, San Diego, Singapore)

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SEARCH FOR IN SILICO APPLICATIONS IN DRUG DISCOVERY AND APPLICATIONS OF DIFFERENT DISCIPLINES IN IT: A SURVEY

SEARCH FOR IN SILICO APPLICATIONS IN DRUG DISCOVERY AND APPLICATIONS OF DIFFERENT DISCIPLINES IN IT: A SURVEY

Bioinformatics is a field of study that analyzes biological data using mathematics, statistics and computer science [37] whereas cheminformatics is the field of study that uses computer and information systems to solve chemical problems [38]. Bioinformatics concentrates on collection, storage, inspection and controlling of biological data whereas cheminformatics does the same for chemical data. Bioinformatics is used to select drug target and helps in the screening process of the candidate drug; not only that, it also helps in determining side effects of a drug and in predicting drug resistance [33]. Structural bioinformatics, a branch of bioinformatics, also have a large contribution in drug discovery. It becomes helpful in analysis and prediction of three dimensional structures of proteins or nucleic acids. The research work of D. K. Brown and O. T. Bishop discusses the role of structural bioinformatics in drug discovery that uses computational SNP analysis [39]. The use of cheminformatics tools to select lead compounds has been discussed in [34].
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Cheminformatics approaches to structure based virtual screening : methodology development and applications

Cheminformatics approaches to structure based virtual screening : methodology development and applications

The goal of this study was to develop robust binary classification QSAR models that would have high predictive power to differentiating binders vs. non-binding ‘decoys’ for AmpC beta-lactamase. We have employed a rigorous validated QSAR modeling workflow that has been developed in our laboratory in recent years. This workflow that incorporates a virtual screening module was applied successfully to several ligand datasets leading to the identification of experimentally confirmed novel hits for different biological targets 112-116 (see recent review 117 ). Herein, we report on classification QSAR models that are capable of discriminating binders from decoys with the external classification accuracy exceeding 90%. Furthermore, we have used these models to screen the compound library tested earlier in the AmpC assay and available from PubChem 118 . We have identified 15 molecules as putative AmpC ligands and demonstrated in subsequent experimental studies that five compounds chosen from these hits were millimolar binders. It worth emphasizing that in all studies reported in this paper we did not use any information on the crystallographic structure of AmpC-ligand complexes and moreover, chemical descriptors were generated from two- dimensional rendering of molecular structures.
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Modern Equivalent Discovery

Modern Equivalent Discovery

A UTHORS: M. A. Hernandez and S. J. Stolfo, The problem of merging multiple databases of information about common entities is frequently encountered in KDD and decision support applications in large commercial and government organizations. The problem we study is often called the Merge/Purge problem and is difficult to solve both in scale and accuracy. Large repositories of data typically have numerous duplicate information entries about the same entities that are difficult to cull together without an intelligent “equational theory” that identifies equivalent items by a complex, domain-dependent matching process. We have developed a system for accomplishing this Data Cleansing task and demonstrate its use for cleansing lists of names of potential customers in a direct marketing-type application. Our results for statistically generated data are shown to be accurate and effective when processing the data multiple times using different keys for sorting on each successive pass. Combing results of individual passes using transitive closure over the independent results, produces far more accurate results at lower cost. The system provides a rule programming module that is easy to program and quite good at finding duplicates especially in an environment with massive amounts of data. This paper details improvements in our system, and reports on the successful implementation for a real-world database that conclusively validates our results previously achieved for statistically generated data. .
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DOCKING AND SCORING IN VIRTUAL SCREENING FOR DRUG DISCOVERY: METHODS AND APPLICATIONS

DOCKING AND SCORING IN VIRTUAL SCREENING FOR DRUG DISCOVERY: METHODS AND APPLICATIONS

why structure-based virtual screening actually ‘works’. The principal reasons are that computational screening is an enrichment process; that accurately calculated energies and scores are not necessarily required for meaningful compound selection; and that appropriate selection strategies compensate for some methodological short- comings. For example, in a typical docking study, a large compound database will probably be reduced to a short- list of preferred candidates, perhaps ~100 or so. To enrich this selection with compounds that have a high proba- bility of being active, de-selection of inappropriate com- pounds (which most of the database compounds are) is as important as finding the most promising candidates. Importantly, de-selection of inappropriate compounds is more easily achieved than selection within the accuracy limitations of the calculations. Also, some binding events, such as those dominated by shape complementarity, can be treated well given the approximations of posing and scoring. Furthermore, as long as active compounds are found in the shortlist, their relative ranking becomes less important. Simply put, an active compound within the top-five scoring compounds will be as good as one within the top 50, as long as these compounds are tested, which further compensates for limitations of scoring. In addi- tion, it is also a rather common practice to subject a reasonably small number of pre-selected candidates (for example, 100–500) to visual inspection, which adds another dimension to the selection process (that is, chemical intuition, knowledge and experience) 81 . So,
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Keynote review: Imaging mass spectrometry: fundamentals and applications to drug discovery

Keynote review: Imaging mass spectrometry: fundamentals and applications to drug discovery

m/z 369 for [M − H 2 O + H] + ) and phospholipid ions were detected by SIMS imaging in distinct areas of brain slices rich in cell bodies. In this study, particularly strong choles- terol signals came from morphological structures contain- ing myelinated axons including the corpus callosum, the anterior commissure, the nucleus triangularis septi and the caudate putamen [ 58 ,68]. These finding are well corroborated with our observation of the cholesterol distribution in mouse spinal cord ( Figure 1 ). In rat kidney sections, an intense cholesterol signal originated from nuclear areas of epithelial cells and the basal lumina of the renal tubes [ 69 ]. Using the molecular imprint-imaging TOF-SIMS method, Sjövall et al. [ 19 ] demonstrated that phosphocholine is predominantly located in the nuclear membrane, and cholesterol is most abundant in the plasma membrane of blood cells. Probably the first example of lipid imaging in a biological sample with MALDI-MS was presented by Laprevote and co-workers [ 70 ]. In this work, differences in lipid composition of regen- erated and normal areas of mdx mouse leg were observed. SIMS imaging provides new insights into membrane biochemistry [ 71 ]. The mechanisms of membrane fusion of conjugating (mating) protozoan Tetrahymena cells were observed by detecting an elevated concentration of the high-curvature lipid 2-aminoethylphosphonolipid in a fusion region [ 72 ]. This finding could lead to a better understanding of the mechanisms of many other cellular events such as endocytosis and exocytosis, where highly curved membrane surfaces are typically formed, and demonstrates the ability to use SIMS for measuring dynamic cellular events of interest when determining the function of small molecules on cellular processes.
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Pharmacogenomics: Applications in Drug Discovery and Pharmacotherapy

Pharmacogenomics: Applications in Drug Discovery and Pharmacotherapy

The technologies such as proteomics and genomics could as the only base for dose adjustments provide potential to identify novel proteins as the targets, different mechanism of action and may provide the different specificity to the drugs. In humans, there are many metabolizing organs but main organ of interest is the liver. In the liver, the metabolism occurs in three phases. In phase I, the compound is turned to more polar nature. Groups such as hydroxyl, N- or 0- may be added to the drugs to make them more polar. This process involves the reduction of cytochrome-bound oxygen and formation of highly- reactive oxyferryl species (Guengerich, 2001; Schlichting et al. 2000). The moieties obtained after the phase I then undergo phase II reaction. In phase II, mainly the conjugation occurs due to glutathione S-transferases. In phase III, the conjugated drugs, if needed could be processed and finally pumped out (Akagah, 2008). The clinical trials are classified into four phases. The drug to be approved for the human use must pass through phases I, II and III. But with pharmacogenetics study during phase I if we study only individual with known genotypes, we can reduce the number of dropouts from the study in the phase III.
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Rapid, precise and reproducible binding affinity prediction: applications in drug discovery

Rapid, precise and reproducible binding affinity prediction: applications in drug discovery

about the possible ligand parameters for the saturated and/or unsaturated rings in PAK4, and indeed, ring structures in BACE1 and ROS1. Repeating ESMACS calculations with different parameters would be beneficial in assessing this, and such a study is currently in progress with AM1-BCC [202] parameterisation method. Binding affinity predictions are a useful asset in the clinical setting, where clini- cians can be supported by accurate and reliable decision-support tools. Binding affinities using the TIES and ESMACS approach were applied to the estrogen re- ceptor (ER) system to understand how these protocols perform for receptors that exhibit mutations – these mutations are the most common in ER-positive breast cancer. Thus, we studied the wild type (WT) ER and two mutant ERs, Y537S and D538G. Six ligands were selected of which one was the endogenous hormone estradiol, and 5 current therapies for ER-positive breast cancer.
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Accelerated Computational Discovery of High-Performance Materials for Organic Photovoltaics by Means of Cheminformatics

Accelerated Computational Discovery of High-Performance Materials for Organic Photovoltaics by Means of Cheminformatics

One of the most important methods is the identification of quantitative structure–property relationships (QSPR). 81–86 This technique has focused intensely in the search for molecules to be experimentally screened as potential drugs, or as drug leads. 87 More recently, QSPR were employed in the study of certain molecules for an understanding of the fundamental processes of cellular and organismic biology. 88,89 In similar fashion to QSPR, quantitative structure-activity relationships (QSAR) are used to study the biological activity of such problems. We note that the complexities faced in the interactions between organic molecules in biological systems are greater than in those found in organic electronic materials. Despite these challenges, cheminformatics has been successful in several areas on the interface between chemistry and biology. 90 For instance, it is possible to analyze the conformation of drug candidates to evaluate their docking potential to a particular biomolecular target and for a prediction of its use as a pharmacophore.
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Advances in molecular modeling and docking as a tool for modern drug discovery

Advances in molecular modeling and docking as a tool for modern drug discovery

Protein tyrosine kinases (tks) are enzymes that catalyze the transfer of phosphate from ATP to tyrosine residues in polypeptides. The human genome contains about 90 TK and 43 TK-like genes, the products of which regulate cellular proliferation, survival, differentiation, function, and motility. Protein kinases represent attractive targets in oncology drug discovery[67]. An interesting class of targets is the erythropoietin-producing human hepatocellular carcinoma receptors (Eph), the largest family of receptor tyrosine kinases. The Eph receptors have been implicated in sprouting angiogenesis and blood vessel remodeling during vascular development. In a recent, study Caflisch et al. have identified three potential tyrosine kinase inhibitors after sequence of virtual screening and docking steps starting with a library of 9 million compounds in the ZINC library. The docked library consisted of about 175 000 compounds derived from nearly 9 million molecules using two-dimensional chemical descriptors and three- dimensional geometric constraints (i.e., relative distance and orientation of pairs of functional groups). Using this procedure, they have identified a series of 5-(piperazine-1-yl)isoquinoline derivatives that exhibited low micromolar affinities for unphosphorylated Abl1 in a competition binding assay [68].
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Virtual screening and its integration with modern drug design technologies

Virtual screening and its integration with modern drug design technologies

Scoring function programs have different parameters which rely on distinct atom type schemes and atomic partial charges calcula- tion methods, and have been trained on diverse ligand-protein data sets. As a result, each program returns a particularly different esti- mate of relative binding affinity, and comparisons are nontrivial. To overcome this limitation, several approaches that assign low ranks to most of inactive compounds while assigning high ranks to most of active compounds have been proposed as alternative methods. One of the most common and employed strategy is to combine estimates from a variety of scoring functions into a single consen- sus score [20]. Several reports show successful applications of this approach to improve hit rates significantly [54,55]. The impact of consensus scoring strategies in the enrichment of true positives (leads) in SBVS can be explained by the fact that the mean of re- peated samplings tends to be closer to the true value than any single sampling, thus, since useful scoring functions perform well, differ- ent methods will vote for some of the same actives [55,56]. This process contributes to a better understanding of the chemistry in- volved in ligand binding and also improves the enrichment of true positives. Consensus scoring is a relatively recent field of research in drug design, with a history of about 10 years. Rapidly, it has become an important tool in the field of in silico technologies for drug discovery. However, significant improvements are required in the efficiency of the consensus scoring methods, mainly regarding the full description of the molecular events involved in the predic- tion of binding affinity. Even though challenging issues such as the consideration of water molecules and protein flexibility are some- what implemented by some docking tools, they still require sub- stantial development to become more useful in drug design. Re- Fig. (5). General docking procedure. Binding mode of the high-affinity selective inhibitor N 6
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Open Source Drug Discovery in Practice: A Case Study

Open Source Drug Discovery in Practice: A Case Study

The two cases operate very differently and differ greatly in magnitude. CSIR OSDD is a vast project, encouraging interna- tional collaboration on its website, but in actuality, geared principally towards Indian researchers and students. The funding from the Indian government applies only to activities within India [24]. There are many workshops and face-to-face meetings in India as well as private e-mail correspondence between teacher and pupil. This, in essence, translates into an Indian-centric project. TSLS, on the other hand, has attracted contributors internationally, albeit substantially fewer than CSIR OSDD, with a variety of motivations. Both have obtained funding used to pay for access to facilities, physical resources and, at times, labor costs. TSLS releases its results into the public domain, where as CSIR OSDD asserts ownership over its results.
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Academic Drug Discovery in the Center for Integrative Chemical Biology and Drug Discovery

Academic Drug Discovery in the Center for Integrative Chemical Biology and Drug Discovery

Center For Integrative Chemical Biology & Drug Discovery CENTER Mission Target Proposals from UNC Faculty “responsive collaborations” ONCOLOGY FOCUS Center Initiated Proj[r]

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To Explain the World: the Discovery of Modern Science

To Explain the World: the Discovery of Modern Science

'I am a physicist, not a historian' (p. ix). This is how Steven Weinberg, one of the most eminent scientists of our time, has chosen to begin his effort to encapsulate the historical development of the scientific method. Starting in the ancient world with a guided tour of Greek physics, and then Greek astronomy, Weinberg touches briefly on the Middle Ages before delving into the Scientific Revolution, finally tying together his grand narrative with an epilogue that takes us forward from Newton to the world of modern science. This is a fluent and scientifically rich book, but it unfortunately fails to engage with the consensus among historians – offering an unabashedly Whig account of the history of science instead.
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Resource discovery and delivery in the modern age

Resource discovery and delivery in the modern age

User perspective: Our students & academics work not only in the library space, but also spend more time in their virtual spaces as compared to physical spaces and they want instant access to information resources, where ever they are. Resource delivery systems have empowered libraries to unify formats, locations, rented & purchased content to provide unified, seamless user experience. For users just the ability to search high quality resources is no use, if it is not delivered just in time. Discovery without delivery is waste of time and frustrating.

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Drug discovery in jeopardy

Drug discovery in jeopardy

companies. Reduced productivity in the drug industry is caused mainly by corporate policies that discourage innovation. This is compounded by various consequences of mega-mergers, the obsession for blockbuster drugs, the shift of control of research from scientists to marketers, the need for fast sales growth, and the discontinuation of

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Toxins and drug discovery

Toxins and drug discovery

While venoms featured in several systems of traditional healing, the modern translation of toxins into medicines began in the 1940s with the introduction of tubocurarine into anaesthetic practice as a selectively acting muscle relaxant (Bowman, 2006). Tubocurarine is one of the key active ingredients in curare, the South American arrow poison. By binding to nicotinic acetylcholine receptors at the neuromuscular junction, tubocurarine blocks the transmission of excitatory signals from motor nerves to skeletal muscles, causing muscle paralysis. Use of tubocurarine allowed patients undergoing major surgery to be paralysed without using dangerously high doses of general anaesthetics. Although this revolutionised anaes- thetic practice, the search soon began for new agents that lacked the cardiovascular side effects of tubocurarine. Since tubocurarine was known to have a relatively rigid core structure carrying two functional groups, most discovery work focused on synthetic compounds with curarimimetic actions: the toxin provided the template for drug design. Relatively little work involved explora- tions of other toxins that could cause paralysis. However, the most successful of the new muscle relaxants, atracurium, did draw on naturally-occurring curare-like alkaloids (Stenlake et al., 1983). Two relatively innocuous moieties were chemically linked to form the active molecule. The chemical bridge was designed to break down
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Cheminformatics and Cyberinfrastructure

Cheminformatics and Cyberinfrastructure

TeraGrid is a facility that integrates computational, information, and analysis resources at the San Diego Supercomputer Center, the Texas Advanced Computing Center, the University of Chicago / Argonne National Laboratory, the National Center for Supercomputing Applications, Purdue University, Indiana University, Oak Ridge National Laboratory, the Pittsburgh

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Nano sponges: A targeted drug delivery system and its applications

Nano sponges: A targeted drug delivery system and its applications

Nanosponge technology is a newer and emerging technology which uses the targeted drug delivery system to release the drug in a controlled manner to the targeted site. Nano sponges are class of materials made up of tiny sponge like structure with narrow cavity of few Nano meter, with an average diameter below 1µm. They cross-link the segments of polyester to form a spherical shape which has many cavities where the drug can be stored. Those narrow cavities can be filled with different type of substance .These are able to carry both hydrophilic and lipophilic drug substances and thereby increasing the solubility of poorly water soluble drug substance [1]. This technology is considered to be a novel approach which offers controlled drug delivery system for topical use. It efficiently offers the entrapment of ingredients with reduced side effects, improved stability, increased elegance and enhanced formulation flexibility [2]. Nanosponges are type of encapsulating nanoparticles which encapsulate the drug molecule within the core by different method of association and it can be classified into encapsulating nanoparticle, complexing nanoparticles, conjugating nanoparticles. When comparing with other nanoparticle, Nano sponges are insoluble in water and organic solvents. Nano sponges are mostly in solid form and it can also be formulated as oral, parenteral, topical or inhalation dosage form. Proteins, peptides, genes, anti-cancer agents and biomolecules have been widely studied using the nanoparticulate system which helps to lower undesired effects and to increase the efficacy.
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