Reviews in Computational Chemistry Volume 18

Application of Scoring Functions in Virtual Screening 69
when docked into the agonist structure, but more importantly, about half of
the agonists are also among the 10% top ranked molecules in the database
when docked into the open, antagonist structure, where they have to compete
with many structures forming salt bridges.
Finding Weak Inhibitors
Seeding experiments are often carried out with a handful of active compounds
that have already been optimized for binding to a given target. Enrichment
factors achieved in this way are often misleading, because finding potent
inhibitors from among a number of random molecules is significantly easier
than distinguishing weakly binding inhibitors from nonbinders. In practice,
virtual screening will find, at best, inhibitors in the low micromolar range,
simply because no chemical database will be large enough, diverse enough,
and lucky enough to find optimized leads right away.
The difficulties associated with weak binders are illustrated in Figure 7
with thrombin as a target. The 7500 compound subset of the WDI mentioned
above was docked into the thrombin active site together with three sets of 100
100
90
80
70
60
50
inhibitors with pK i 7-9
inhibitors with pK i 5-7
inhibitors with pK i 4-5
40
1 2 3 4 5 10 20 30 50 100
Figure 7 Enrichment of three sets of 100 thrombin inhibitors that cover different ranges
of activity. Less active compounds are more difficult to enrich.