| Abstract
| - To be considered for further development, lead structures should display the following properties: (1) simplechemical 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 thosethat are marketed drugs themselves but have been altered to yield novel drugs. We have previously analyzedthe design of leadlike combinatorial libraries starting from 18 lead and drug pairs of structures (S. J. Teagueet al. Angew. Chem., Int. Ed. Engl.1999, 38, 3743−3748). Here, we report results based on an extendeddataset of 96 lead-drug pairs, of which 62 are lead structures that are not marketed as drugs, and 75 aredrugs 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 rotatablebonds), the number of hydrogen bond donors (HDO) and acceptors (HAC), the calculated logarithm of then-octanol/water partition (CLogP), the calculated logarithm of the distribution coefficient at pH 7.4 (LogD74),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: ΔMW = 69; ΔCMR =1.8; ΔRNG = ΔHAC =1; ΔRTB = 2; ΔCLogP = 0.43; ΔLogD74 = 0.97; ΔHDO = 0; ΔDFPS = 0.15;ΔPPFS = 0.12. Lead structures exhibit, on the average, less molecular complexity (less MW, less numberof rings and rotatable bonds), are less hydrophobic (lower CLogP and LogD74), and less druglike (lowerdruglike scores). These findings indicate that the process of optimizing a lead into a drug results in morecomplex structures. This information should be used in the design of novel combinatorial libraries that areaimed at lead discovery.
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