Bioinformatics in Drug Discovery a Review <s> IV. DRUG TARGET IDENTIFICATION <s> BACKGROUND ::: Drug discovery is the process of discovering and designing drugs, which includes target identification, target validation, lead identification, lead optimization and introduction of the new drugs to the public. This process is very important, involving analyzing the causes of the diseases and finding ways to tackle them. ::: ::: ::: OBJECTIVE ::: The problems we must face include: i) that this process is so long and expensive that it might cost millions of dollars and take a dozen years; and ii) the accuracy of identification of targets is not good enough, which in turn delays the process. Introducing bioinformatics into the drug discovery process could contribute much to it. Bioinformatics is a booming subject combining biology with computer science. It can explore the causes of diseases at the molecular level, explain the phenomena of the diseases from the angle of the gene and make use of computer techniques, such as data mining, machine learning and so on, to decrease the scope of analysis and enhance the accuracy of the results so as to reduce the cost and time. ::: ::: ::: METHODS ::: Here we describe recent studies about how to apply bioinformatics techniques in the four phases of drug discovery, how these techniques improve the drug discovery process and some possible difficulties that should be dealt with. ::: ::: ::: RESULTS ::: We conclude that combining bioinformatics with drug discovery is a very promising method although it faces many problems currently. <s> BIB001 </s> Bioinformatics in Drug Discovery a Review <s> IV. DRUG TARGET IDENTIFICATION <s> Identification of potential drug targets is the first step in the process of modern drug discovery, subjected to their validation and drug development. Whole genome sequences of a number of organisms allow prediction of potential drug targets using sequence comparison approaches. Here, we present a subtractive approach exploiting the knowledge of global gene expression along with sequence comparisons to predict the potential drug targets more efficiently. Based on the knowledge of 155 known virulence and their coexpressed genes mined from microarray database in the public domain, 357 coexpressed probable virulence genes for Vibrio cholerae were predicted. Based on screening of Database of Essential Genes using blastn, a total of 102 genes out of these 357 were enlisted as vitally essential genes, and hence good putative drug targets. As the effective drug target is a protein which is only present in the pathogen, similarity search of these 102 essential genes against human genome sequence led to subtraction of 66 genes, thus leaving behind a subset of 36 genes whose products have been called as potential drug targets. The gene ontology analysis using Blast2GO of these 36 genes revealed their roles in important metabolic pathways of V. cholerae or on the surface of the pathogen. Thus, we propose that the products of these genes be evaluated as target sites of drugs against V. cholerae in future investigations. <s> BIB002 </s> Bioinformatics in Drug Discovery a Review <s> IV. DRUG TARGET IDENTIFICATION <s> Motivation:In silico prediction of drug–target interactions from heterogeneous biological data is critical in the search for drugs and therapeutic targets for known diseases such as cancers. There is therefore a strong incentive to develop new methods capable of detecting these potential drug–target interactions efficiently. ::: ::: Results: In this article, we investigate the relationship between the chemical space, the pharmacological space and the topology of drug–target interaction networks, and show that drug–target interactions are more correlated with pharmacological effect similarity than with chemical structure similarity. We then develop a new method to predict unknown drug–target interactions from chemical, genomic and pharmacological data on a large scale. The proposed method consists of two steps: (i) prediction of pharmacological effects from chemical structures of given compounds and (ii) inference of unknown drug–target interactions based on the pharmacological effect similarity in the framework of supervised bipartite graph inference. The originality of the proposed method lies in the prediction of potential pharmacological similarity for any drug candidate compounds and in the integration of chemical, genomic and pharmacological data in a unified framework. In the results, we make predictions for four classes of important drug–target interactions involving enzymes, ion channels, GPCRs and nuclear receptors. Our comprehensively predicted drug–target interaction networks enable us to suggest many potential drug–target interactions and to increase research productivity toward genomic drug discovery. ::: ::: Supplementary information: Datasets and all prediction results are available at http://cbio.ensmp.fr/~yyamanishi/pharmaco/. ::: ::: Availability: Softwares are available upon request. ::: ::: Contact: yoshihiro.yamanishi@ensmp.fr <s> BIB003 </s> Bioinformatics in Drug Discovery a Review <s> IV. DRUG TARGET IDENTIFICATION <s> There have been several success stories in the field of pharmacogenetics in recent years, including the analysis of HER2 amplification for trastuzumab selection in breast cancer and VKORC1 genotyping for warfarin dosing in thrombosis. Encouraging results from these studies suggest that genetic factors may indeed be important determinants of drug response and toxicity for at least some drugs. However, to apply pharmacogenetics appropriately, a thorough understanding of the scope and limitations of this field is required. The challenges include an appreciation of biological variability, logistical issues pertaining to the proper management of information, the need for robust methods and adequate sample quality with well-designed workflows. At the same time, the economics of pharmacogenetic testing from the perspective of clinicians, patients, governments, insurance companies and pharmaceutical companies will play an important role in determining its future use. Ethical considerations such as informed consent and patient privacy, as well as the role of regulatory bodies in addressing these issues, must be fully understood. Only once these issues are properly dealt with can the full benefits of pharmacogenetics begin to be realised. <s> BIB004 </s> Bioinformatics in Drug Discovery a Review <s> IV. DRUG TARGET IDENTIFICATION <s> Abstract: The process of bringing new and innova-tive drugs, from conception and synthesis through to approval on the market can take the pharmaceutical industry 8 – 15 years and cost approximately $1.8 bil-lion. Two key technologies are improving the hit-to-drug timeline: high-throughput screening (HTS) and rational drug design. In the latter case, starting from some known ligand-based or target-based information, a lead structure will be rationally designed to be tested in vitro or in vivo . Computational methods are part of many drug discovery programs, including the assessment of ADME (absorption-distribution-metabolism-excretion) and toxicity (ADMET) properties of compounds at the early stages of discovery/development with impressive results. The aim of this paper is to review, in a simple way, some of the most popular strategies used by model-ers and some successful applications on computational chemistry to raise awareness of its importance and potential for an actual multidisciplinary drug discovery process .Keywords: dockin g; <s> BIB005
The current approach is to identify the biologically active candidates  . Drugs are usually only developed when the particular drug target for those drugs" actions have been identified and studied. The number of potential targets for drug discovery process is increasing exponentially. Mining and warehousing of the human genome sequence using bioinformatics has helped to define and classify the nucleotide compositions of those genes, which are responsible for the coding of target proteins, in addition to identifying new targets that offer more potential for new drugs BIB001 , BIB002 . This is an area where the human genome information is expected to play a master role BIB003 . Drug developers developed the unnatural good quality of more genes are identified and the drug discovery cycle becomes more data-intensive BIB004 . Bioinformatics is used to identify and analyse more and more biological drug targets; thus expected to greatly increase the breath of possible drugs in the pipelines of pharmaceutical companies  , BIB005 . Bioinformatics also provides strategies and algorithm to predict new drug targets and to store and control available drug target information. After the discovery of ""potential"" drug targets, there is an insignificant need to establish a strong association between a putative target and disease of interest BIB003 . The establishment of such a key association provides justification for the drug improvement process this process is known as target validation. It is an area where bioinformatics play a vital role (Fig. 1) . The possibility for failure in the clinical testing and approval phases can be moderated by Drug target validation  ,  ,  . 