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What makes a good fragment in fragment-based drug discovery?

Over the past 25 years, fragment-based drug discovery (FBDD) has emerged as a highly successful, complementary approach to high-throughput screening (HTS) to identify quality leads for optimization into clinical candidates and approved medicines. Four drugs on the market and over 40 new chemical entities in clinical trials discovered via FBDD campaigns have established their place in the armamentarium of medicinal chemistry, and most pharmaceutical and biotechnology companies, along with many academic institutions, have implemented FBDD [1]. The theoretical basis of FBDD was established by the concept of using ‘small pieces of chemistry’ that are found to bind to the active sites of proteins [2]. This idea was advanced by the observation that individual functional groups contributed to the energy of binding between a drug and its protein target [3]. Another theoretical premise (the complexity model) postulates that there is a higher probability of a match between a ligand and its receptor if there are fewer interactions to get right, and higher complexity can lead to unfavorable interactions. Balanced with the probability of detection by biophysical assay methods, this leads to the concept that two to three pharmacophoric interactions per fragment are optimal [4]. Fragment libraries sample a much larger chemical space than HTS libraries due to their smaller size, and a fragment library of only a few thousand members can routinely provide quality hits for hit to lead campaigns [5]. Furthermore, hit rates from fragment screens are often used to assess whether a biological target is ‘ligandable’ and potentially druggable [6]. The emerging success of FBDD has increased expectations of addressing more challenging new target classes, such as protein-protein and protein–DNA interactions. Typical fragment hits have weak affinities, with equilibrium dissociation constant (Kd) values ranging from 1 mM to 100 μM, as compared with HTS hits, which have Kd values in the nM to 10 μM range [6]. Such weak affinities are difficult to measure in standard biochemical assays used for HTS screens, so more sensitive biophysical techniques have been developed and successfully used in fragment screening: NMR, surface plasmon resonance, X-ray crystallography, thermal shift, affinity capture, and others [7]. At least two orthogonal methods are used for validating screening hits. Thus, fragments need to be highly soluble (>10 mM in dimethyl sulfoxide) for screening at such high concentrations. FBDD uses fragment linking, merging, and growing approaches during hit to lead and lead optimization stages of a discovery program. Structure-based drug design is normally used in most fragment-based optimizations to help guide synthetic efforts to increase binding affinity and navigate physicochemical properties [8]. Another fragment-based metric, ligand efficiency (LE), has evolved to monitor affinity improvement so that increase in molecular weight and lipophilicity remain within productive ranges [9]. LE is a ratio of affinity divided by the number of heavy atoms: a value of >0.3 is considered desirable.