Proteins are in constant motion between different conformational states with similar energies. This has often been ignored in drug design. However, protein flexibility is fundamental to understanding the ways in which drugs exert biological effects, their binding-site location, binding orientation, binding kinetics, metabolism and transport. Protein flexibility allows increased affinity to be achieved between a drug and its target. This is crucial, because the lipophilicity and number of polar interactions allowed for an oral drug is limited by absorption, distribution, metabolism and toxicology considerations.

To be considered for further development, lead structures should display the following properties: (1) simple chemical features, amenable for chemistry optimization; (2) membership to an established SAR series; (3) favorable patent situation; and (4) good absorption, distribution, metabolism, and excretion (ADME) properties. There are two distinct categories of leads: those that lack any therapeutic use (i.e., "pure" leads), and those that are marketed drugs themselves but have been altered to yield novel drugs. We have previously analyzed the design of leadlike combinatorial libraries starting from 18 lead and drug pairs of structures (S. J. Teague et al. Angew. Chem., Int. Ed. Engl. 1999, 38, 3743-3748). Here, we report results based on an extended dataset of 96 lead-drug pairs, of which 62 are lead structures that are not marketed as drugs, and 75 are drugs that are not presumably used as leads. We examined the following properties: MW (molecular weight), CMR (the calculated molecular refractivity), RNG (the number of rings), RTB (the number of rotatable bonds), the number of hydrogen bond donors (HDO) and acceptors (HAC), the calculated logarithm of the n-octanol/water partition (CLogP), the calculated logarithm of the distribution coefficient at pH 7.4 (LogD(74)), the Daylight-fingerprint druglike score (DFPS), and the property and pharmacophore features score (PPFS). The following differences were observed between the medians of drugs and leads: DeltaMW = 69; DeltaCMR = 1.8; DeltaRNG = DeltaHAC =1; DeltaRTB = 2; DeltaCLogP = 0.43; DeltaLogD(74) = 0.97; DeltaHDO = 0; DeltaDFPS = 0.15; DeltaPPFS = 0.12. Lead structures exhibit, on the average, less molecular complexity (less MW, less number of rings and rotatable bonds), are less hydrophobic (lower CLogP and LogD(74)), and less druglike (lower druglike scores). These findings indicate that the process of optimizing a lead into a drug results in more complex structures. This information should be used in the design of novel combinatorial libraries that are aimed at lead discovery.

The optimization of low-potency leads into drugs is often accompanied by an increase in molecular weight (M(r)) and lipophilicity, as a consequence of affinity enhancement. Hits with affinity at µM levels discovered by screening leadlike libraries allow scope for this optimization process, as shown schematically by the distributions of M(r) for a leadlike library (1), oral drugs (2), and a typical combinatorial chemistry library (3). y=percentage with a particular molecular weight.