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| Titre : | Data Mining in Drug Discovery |
| Auteurs : | Rémy D. Hoffmann |
| Type de document : | document électronique |
| Editeur : | [S.l.] : Wiley-VCH, 2013 |
| Résumé : |
Written for drug developers rather than computer scientists, this monograph adopts a systematic approach to mining scientific data sources, covering all key steps in rational drug discovery, from compound screening to lead compound selection and personalized medicine. After a general introduction to the topic, common data mining tools for chemical, biological, text and other data types are described, including second order tools that combine information from multiple sources. The next section compares the different data sources available, both commercial and non-commercial. The main section of the book is devoted to the most common drug discovery applications where data mining can substantially enhance the research effort. Throughout the book, industrial and academic drug discovery strategies are addressed, with contributors coming from both areas, enabling an informed decision on when and which data mining tools to use for one's own drug discovery project. ### Review ÔÇ£In summary, the book reflects the state-of-the-art for a rapidly changing field, with key emergent themes being the accessibility of public data, multiassay end-points for compounds, and the need to interpret these in the context of complex biological systems. It also usefully highlights some of the research challenges, with pointers to key likely future progress.ÔÇØ┬á (*ChemMedChem*, 1 June 2014) ### From the Back Cover Written for drug developers rather than computer scientists, this monograph adopts a systematic approach to mining scientific data sources, covering all key steps in rational drug discovery, from compound screening to lead compound selection and personalized medicine. Clearly divided into four sections, the first part discusses the different data sources available, both commercial and non-commercial, while the next section looks at the role and value of data mining in drug discovery. The third part compares the most common applications and strategies for polypharmacology, where data mining can substantially enhance the research effort. The final section of the book is devoted to systems biology approaches for compound testing. Throughout the book, industrial and academic drug discovery strategies are addressed, with contributors coming from both areas, enabling an informed decision on when and which data mining tools to use for one?s own drug discovery project. |
