Reviews in Computational Chemistry Volume 18

70 The Use of Scoring Functions in Drug Discovery Applications
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known inhibitors in different activity ranges. It can be clearly seen in Figure 7
that enrichment decreases as the binding affinity of the active compounds
decreases. Note that thrombin is a relatively easy target for most virtual
screening methods (at least to identify compounds with charged moieties binding
to the S1 subsite), and thus the separation of actives and inactives is still
good for the low micromolar inhibitors. According to the authors’ experience,
the situation is worse for many other targets.
Nevertheless, library ranking can successfully be applied to enrich even
very weak ligands. A database of approximately 4000 commercially available
compounds was screened against FKBP by means of the SAR-by-NMR technique
202 and was found to contain 31 compounds with activities below 2 mM.
Three examples of these compounds are shown in Figure 8. Compounds 5, 6,
and 7 have measured dissociation constants of 0.1, 0.13 and 0.5 mM, respectively.
This set of structures was flexibly docked into the FKBP binding site
using DOCK 4.0 in conjunction with the PMF scoring function. 87 For the top
20% of the ranked database, enrichment factors between 2 and 3 were
achieved. Enrichment factors were twice as large as those obtained with the
standard AMBER score implemented in DOCK.
Consensus Scoring
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NH
5 6 7
Figure 8 Weak binders to FKBP.
Different scoring schemes emphasize different physical phenomena that
are important for ligand binding. Differences between scoring schemes might
not be obvious in the calculation of binding affinities for known active compounds,
but they can be very pronounced in the assessment of nonbinding
molecules. The computational group at Vertex has reported good experience
with a concept called ‘‘consensus scoring,’’ whereby libraries of molecules are
docked and assessed with several scoring functions and only those molecules
are retained that score well with the majority of those functions. This can lead
to a significant decrease in false positives, 198 but invariably a number of true
positives is also lost in the process (see, e.g., Ref. 77).
One should keep in mind that in consensus scoring the number of false
positives can be reduced, but one runs the risk of eliminating a number of
active compounds that only one of the scoring functions has ranked high. Consider
the example of the p38 MAP kinase inhibitors in Figure 4: consensus