Reviews in Computational Chemistry Volume 18

Scoring Functions for Receptor–Ligand Interactions 55
hits in virtual screening applications. Reducing the weight can be done by
reducing charges of surface residues when explicit electrostatic terms are
used 100 or by multiplying the hydrogen-bond by a factor that depends on the
accessibility of the protein-bonding partner 91 in empirical scoring functions.
Ionic interactions are treated in a similar manner as hydrogen bonds.
Long-distance charge–charge interactions are usually disregarded, and so it
is more appropriate to refer to salt bridges or charged hydrogen bonds here.
The SCORE1 function by Boehm implemented in LUDI 56 gives greater weight
to salt bridges than to neutral hydrogen bonds. This function was found to be
useful in scoring several series of thrombin inhibitors. 57,72 But just as with
force field scoring functions, this weighting introduces the danger of giving
unreasonably high scores to highly charged molecules. Our experience with
the docking program FlexX, 64–67 which contains a variant of SCORE1 (the
LUDI scoring function), has been that better results are generally obtained
when charged and uncharged hydrogen are treated equally in virtual screening
applications. This observation is also the case for the ChemScore function by
Protherics. 71
Hydrophobic interaction energies are usually estimated by the size of the
contact surface at the receptor–ligand interface. A reasonable correlation
between experimental binding energies can often be achieved with a surface
term alone (see, e.g., Refs. 24,153,154 and the discussion in the earlier section
on Major Contributions to Protein–Ligand Interactions). Various approximations
for surface terms have been used, such as grid-based methods 56 and
volume-based methods (see especially the discussion in Ref. 101). Many functions
employ distance-scaled sums over neighboring receptor–ligand atom
pairs. Distance cutoffs for these functions have been chosen to be short 64 or
to be longer to include atom pairs that do not form direct van der Waals contacts.
62,71 The assignment of the weighting factor Gi for the hydrophobic
term depends strongly on the training set. Its value might have been underestimated
in most derivations of empirical scoring functions, 155 because most
training sets contain an overly large proportion of ligands containing an excessive
number of donor and acceptor groups (many peptide and carbohydrate
fragments).
In most existing empirical scoring functions, a number of atom types are
defined as being hydrophobic, and all their contributions are treated in the
same manner. Alternatively, the propensity of specific atom types to be located
in the solvent or in the interior of a protein can be assessed by so-called
‘‘atomic solvation parameters’’ that can be derived from experimental data
such as octanol–water partition coefficients 156,157 or from structural data. 81,158
Atomic solvation parameters are used in the VALIDATE scoring function, 68
and they have been tested in DOCK. 80 Entropy terms in empirical scoring
functions account for the restriction of conformational degrees of freedom
of the ligand upon complex formation. A crude but useful estimate of this
entropy contribution is the number of freely rotatable bonds of a ligand.