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      On the Integration of In Silico Drug Design Methods for Drug Repurposing

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          Abstract

          Drug repurposing has become an important branch of drug discovery. Several computational approaches that help to uncover new repurposing opportunities and aid the discovery process have been put forward, or adapted from previous applications. A number of successful examples are now available. Overall, future developments will greatly benefit from integration of different methods, approaches and disciplines. Steps forward in this direction are expected to help to clarify, and therefore to rationally predict, new drug–target, target–disease, and ultimately drug–disease associations.

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          Most cited references46

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          The Protein Data Bank.

          The Protein Data Bank (PDB; http://www.rcsb.org/pdb/ ) is the single worldwide archive of structural data of biological macromolecules. This paper describes the goals of the PDB, the systems in place for data deposition and access, how to obtain further information, and near-term plans for the future development of the resource.
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            The protein kinase complement of the human genome.

            G. Manning (2002)
            We have catalogued the protein kinase complement of the human genome (the "kinome") using public and proprietary genomic, complementary DNA, and expressed sequence tag (EST) sequences. This provides a starting point for comprehensive analysis of protein phosphorylation in normal and disease states, as well as a detailed view of the current state of human genome analysis through a focus on one large gene family. We identify 518 putative protein kinase genes, of which 71 have not previously been reported or described as kinases, and we extend or correct the protein sequences of 56 more kinases. New genes include members of well-studied families as well as previously unidentified families, some of which are conserved in model organisms. Classification and comparison with model organism kinomes identified orthologous groups and highlighted expansions specific to human and other lineages. We also identified 106 protein kinase pseudogenes. Chromosomal mapping revealed several small clusters of kinase genes and revealed that 244 kinases map to disease loci or cancer amplicons.
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              The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease.

              To pursue a systematic approach to the discovery of functional connections among diseases, genetic perturbation, and drug action, we have created the first installment of a reference collection of gene-expression profiles from cultured human cells treated with bioactive small molecules, together with pattern-matching software to mine these data. We demonstrate that this "Connectivity Map" resource can be used to find connections among small molecules sharing a mechanism of action, chemicals and physiological processes, and diseases and drugs. These results indicate the feasibility of the approach and suggest the value of a large-scale community Connectivity Map project.
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                Author and article information

                Contributors
                Journal
                Front Pharmacol
                Front Pharmacol
                Front. Pharmacol.
                Frontiers in Pharmacology
                Frontiers Media S.A.
                1663-9812
                23 May 2017
                2017
                : 8
                : 298
                Affiliations
                [1] 1Molecular Modelling & Drug Design Lab, Department of Life Sciences, University of Modena and Reggio Emilia Modena, Italy
                [2] 2Discovery Sciences, Innovative Medicines and Early Development Biotech Unit, AstraZeneca R&D Gothenburg Mölndal, Sweden
                Author notes

                Edited by: Yuhei Nishimura, Mie University, Japan

                Reviewed by: Antonio Macchiarulo, University of Perugia, Italy; Yoshito Zamami, Tokushima University Graduate School of Medical Sciences, Japan

                *Correspondence: Giulio Rastelli, giulio.rastelli@ 123456unimore.it

                This article was submitted to Experimental Pharmacology and Drug Discovery, a section of the journal Frontiers in Pharmacology

                Article
                10.3389/fphar.2017.00298
                5440551
                28588497
                0e13ff75-2bb4-44b2-bea4-7d820f02dfff
                Copyright © 2017 March-Vila, Pinzi, Sturm, Tinivella, Engkvist, Chen and Rastelli.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 07 April 2017
                : 10 May 2017
                Page count
                Figures: 1, Tables: 0, Equations: 0, References: 46, Pages: 7, Words: 0
                Funding
                Funded by: H2020 Marie Skłodowska-Curie Actions 10.13039/100010665
                Award ID: No 676434, “Big Data in Chemistry” (BIGCHEM)
                Categories
                Pharmacology
                Perspective

                Pharmacology & Pharmaceutical medicine
                drug repurposing,drug discovery,molecular modeling,chemogenomics,structure-based drug design,ligand-based drug design,machine learning,transcriptomics

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