Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5521085 | Drug Discovery Today | 2017 | 12 Pages |
Abstract
During the preliminary stage of a drug discovery project, the lack of druggability information and poor target selection are the main causes of frequent failures. Elaborating on accurate computational druggability prediction methods is a requirement for prioritizing target selection, designing new drugs and avoiding side effects. In this review, we describe a survey of recently reported druggability prediction methods mainly based on networks, statistical pocket druggability predictions and virtual screening. An application for a frequent mutation of p53 tumor suppressor is presented, illustrating the complementarity of druggability prediction approaches, the remaining challenges and potential new drug development perspectives.
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Authors
Hiba Abi Hussein, Colette Geneix, Michel Petitjean, Alexandre Borrel, Delphine Flatters, Anne-Claude Camproux,