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Fragment-Based Drug Discovery

Authors: Raymond S. Norton;

Fragment-Based Drug Discovery

Abstract

Fragment-based drug discovery (FBDD), while still a relatively new approach, has been so successful for identifying ligands for proteintargetsthatitisalreadywidelyregardedasrepresentinga sea-change in drug discovery techniques. The strategy involves identifying small (typically ,300Da), low-affinity ligands (‘fragments’) and combining or expanding these to produce larger, higher-affinity ligands. The major advantage of FBDD over more traditional high-throughput screening is that FBDD providesamorerapidandeffectivemeansofidentifyingligands for a protein target. Because there are fewer possible fragmentsized molecules than lead- or drug-sized molecules, FBDD samples chemical space far more efficiently than traditional approaches and therefore requires far fewer compounds to be testedtoidentifysuitablehitsasstartingpointsfordevelopment. Furthermore, fragment-based screeningtypicallyprovidesmore ‘developable’ compounds than traditional drug discovery approaches,whichoptimiseamedium-tohigh-affinityhit.Most importantly, fragment methods produce lead candidates with physicochemical properties (described by Lipinski’s ‘rule of five’) that are likely to result in orally bioavailable compounds. FBDD also has the capability of developing inhibitors of protein–protein interactions (PPIs), about which the pharmaceutical industry has had major reservations in the past as drug targets; that skepticism, however, is gradually being eroded as blockers of such interactions progress to the clinic. Indeed, the recent approval of vemurafenib, a B-Raf(V600E) inhibitordevelopedbyPlexxikonforlate-stagemelanoma,validates

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Average
Average
Average
bronze