Reviews in Computational Chemistry Volume 18

Application of Scoring Functions in Virtual Screening 65
also teach how to recognize typical failure cases. Recent examples of library
ranking experiments include those by Charifson et al., 198 Bissantz, Folkers,
and Rognan, 77 and Stahl and Rarey. 78 Charifson and co-workers compiled
sets of several hundred active molecules for three different targets: p38
MAP kinase, inosine monophosphate dehydrogenase, and HIV protease.
The members of these sets were then docked into the corresponding active sites
together with 10,000 randomly chosen, but drug-like, commercial compounds
using DOCK 98 and the Vertex in-house docking tool Gambler. Three scoring
functions performed consistently well in enriching active compounds, namely,
ChemScore, 71,199 the DOCK AMBER force field score, and PLP. 62 The finding
that these three scoring functions performed so well was partially attributed
to the fact that a rigid-body optimization could be carried out with these
functions, because the functions include repulsive terms in contrast to many
of the other tested functions. The study by Stahl and Rarey 78 compared the
performance of DrugScore 92 and PMF 86 to that of PLP 62 and FlexX score
using the docking program FlexX. 64–66 Interestingly, the two knowledgebased
scoring functions showed significantly different behavior for extreme
cases of active sites. DrugScore coped well with situations where ligands are
tightly bound in narrow lipophilic cavities (e.g., COX-2 and the thrombin
S1 pocket), whereas PMF did not lead to good enrichment in such cases. Conversely,
for the very polar binding site of neuraminidase, PMF gave better
enrichment than any other scoring function, whereas DrugScore failed. The
description of complexes in which many hydrogen bonds play a role seems
to be a general strength of PMF. This has also been noted by Bissantz, Folkers
and Rognan, 77 who found PMF to perform well for the polar target thymidine
kinase and less well for the estrogen receptor.
Hydrogen Bonding versus Hydrophobic Interactions
It is of central importance in virtual screening to achieve a balanced
description of hydrogen bonding and hydrophobic contributions to the score
in order to avoid a bias toward either highly polar or completely hydrophobic
molecules. Empirical scoring functions have the advantage that they can be
quickly reparameterized to achieve such a balance, whereas such an adjustment
is impossible with knowledge-based functions. Because this is such an
important topic, we will illuminate it with a number of examples.
Consider the following database ranking experiment. A database of
about 7600 compounds was flexibly docked into the ATP binding site of
p38 MAP kinase. The database consisted of ca. 7500 random compounds
from the World Drug Index (WDI) 200 and 72 inhibitors of p38 MAP kinase,
which in turn consisted of 30 inhibitors forming two hydrogen bonds with
the receptor and 20 inhibitors forming only one. Both groups covered the
same activity range from low micromolar (mM) to about 10 nM. For each
of the docked compounds, up to 800 alternative docking solutions were