Drug Metabolism and Personalized Therapy
Official journal of the European Society of Pharmacogenomics and Personalised Therapy
Editor-in-Chief: Llerena, Adrián
Editorial Board Member: Benjeddou, Mongi / Chen, Bing / Dahl, Marja-Liisa / Devinsky, Ferdinand / Hirata, Rosario D. C. / Hubacek, Jaroslav A. / Ingelman-Sundberg, Magnus / Maitland-van de Zee, Anke-Hilse / Manolopoulos, Vangelis G. / Marc, Janja / Melichar, Bohuslav / Meyer, Urs A. / Nair, Sujit / Nofziger, Charity / Peiro, Ana / Sadee, Wolfgang / Salazar, Luis A. / Simmaco, Maurizio / Turpeinen, Miia / Schaik, Ron / Shin, Jae-Gook / Siest, Gérard / Visvikis-Siest, Sophie / Zanger, Ulrich M.
SCImago Journal Rank (SJR) 2015: 0.447
Source Normalized Impact per Paper (SNIP) 2015: 0.524
Impact per Publication (IPP) 2015: 1.329
In silico pharmacology for a multidisciplinary drug discovery process
1Departamento de Química Farmacéutica y Orgánica, Facultad de Farmacia, Universidad de Granada, Granada, España
Citation Information: Drug Metabolism and Drug Interactions. Volume 27, Issue 4, Pages 199–207, ISSN (Online) 2191-0162, ISSN (Print) 0792-5077, DOI: 10.1515/dmdi-2012-0021, November 2012
- Published Online:
The process of bringing new and innovative drugs, from conception and synthesis through to approval on the market can take the pharmaceutical industry 8–15 years and cost approximately $1.8 billion. 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 modelers and some successful applications on computational chemistry to raise awareness of its importance and potential for an actual multidisciplinary drug discovery process.
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