Hit and Lead Identification from Fragments
Michael P. Mazanetz, Richard Law, Mark A. Whittaker · 2013
In recent centenary, the primary driver in the optimization of hits to drug candidates, usually derived by some form of screening, was the generation of structure–activity relationships (SARs) for the primary target, off-targets, and ADMET properties. In the first decade of the new millennium, this multiparameter optimization (MPO) approach has developed beyond optimization of SAR to encompass also the optimization of structure–property relationships (SPropRs) [1] and of structure–efficiency relationships (SERs). The former has been inspired by the retrospective analysis of large datasets of known drugs and proprietary molecules to identify drug-like physicochemical space [2], whereas the latter have been developed through the widespread adoption of low molecular (fragments) as starting points for lead identification. Fragment techniques, where they have been used as the sole means of lead generation, have been variously referred to as fragment-based drug discovery (FBDD) [3], fragment-based lead discovery (FBLD) [4], fragment-based ligand discovery (FBLD) [5], and fragment-based lead generation (FBLG) [6]. Where fragment methods have been used in concert with other techniques for hit finding and optimization, the terms fragment-assisted drug discovery (FADD) [7] and fragment-inspired medicinal chemistry (FIMC) [8] have been applied. Whatever the acronym used, the approach generically involves screening compounds of smaller size (fragments) to identify weakly active hit compounds followed by optimization to lead series guided by structural insights from protein–ligand X-ray crystallography. A particular advantage of the fragment approach is that by starting from the most ligand efficient starting points MPO of SAR, SER, and SPropR can be addressed from the outset to deliver effective drug candidates with lower propensity to suffer attrition due to nondrug-like characteristics. This review focuses on the design aspects of FBLG methods and reflects our experience of developing and applying strategies for design from fragments, with a particular emphasis on the use of computational techniques to identify the best fragments to progress and to assist with their MPO.