Bioinformatics in Drug Discovery a Review <s> INTRODUCTION <s> Modern drug discovery is characterized by the production of vast quantities of compounds and the need to examine these huge libraries in short periods of time. The need to store, manage and analyze these rapidly increasing resources has given rise to the field known as computer-aided drug design (CADD). CADD represents computational methods and resources that are used to facilitate the design and discovery of new therapeutic solutions. Digital repositories, containing detailed information on drugs and other useful compounds, are goldmines for the study of chemical reactions capabilities. Design libraries, with the potential to generate molecular variants in their entirety, allow the selection and sampling of chemical compounds with diverse characteristics. Fold recognition, for studying sequence-structure homology between protein sequences and structures, are helpful for inferring binding sites and molecular functions. Virtual screening, the in silico analog of high-throughput screening, offers great promise for systematic evaluation of huge chemical libraries to identify potential lead candidates that can be synthesized and tested. In this article, we present an overview of the most important data sources and computational methods for the discovery of new molecular entities. The workflow of the entire virtual screening campaign is discussed, from data collection through to post-screening analysis. <s> BIB001 </s> Bioinformatics in Drug Discovery a Review <s> INTRODUCTION <s> The increased availability of large-scale open-access resources on bioactivities of small molecules has a significant impact on pharmacology facilitated mainly by computational approaches that digest the vast amounts of data. We discuss here how computational data integration enables systemic views on a drug's action and allows to tackle complex problems such as the large-scale prediction of drug targets, drug repurposing, the molecular mechanisms, cellular responses or side effects. We particularly focus on computational methods that leverage various cell-based transcriptional, proteomic and phenotypic profiles of drug response in order to gain a systemic view of drug action at the molecular, cellular and whole-organism scale. <s> BIB002 </s> Bioinformatics in Drug Discovery a Review <s> INTRODUCTION <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> BIB003
HE drug discovery process was beginning in 19 th century by John Langley in 1905 when he proposed the theory of respective substances. The first rational development of synthetic drugs was carried out by Paul Ehrlich (father of modern chemotherapy) and SacachiroHata who produced arsphenamine(Salvarsan) in 1910 by structure activity relationship from atoxyl used in sleeping sickness (trypanosomiasis) and syphilis  . Drug discovery is the stepby-step process by which new candidate drugs are discovered. Normally, pharmaceutical companies follow well-established pharmacology and chemistry-based drugdiscovery approaches, and face various difficulties in finding new drugs BIB002 .The aim of drug discovery is to produce more and more drugs in a short period of time with low risk in bioinformatics BIB003 . In fact, now there is an existence of new, separate field, known as computer-aided drug design (CADD), BIB001 ,  . 