ABSTRACT: Fast and accurate predicting of the binding
affinities of large sets of diverse protein-ligand complexes
is an important, yet extremely challenging, task in drug
discovery. The development of knowledge-based scoring
functions exploiting structural information of known
protein-ligand complexes represents a valuable contribution
to such a computational prediction. In this study, we
report a scoring function named IPMF that integrates
additional experimental binding affinity information into
the extracted potentials, on the assumption that a scoring
function with the “enriched” knowledge base may achieve
increased accuracy in binding affinity prediction. In our
approach, the functions and atom types of PMF04 were
inherited to implicitly capture binding effects that are hard
to model explicitly, and a novel iteration device was designed
to gradually tailor the initial potentials. We evaluated
the performance of the resultant IPMF with a diverse set
of 219 protein-ligand complexes and compared it with seven scoring functions commonly used in computer-aided drug
design, includingGLIDE,AutoDock4,VINA, PLP, LUDI, PMF, and PMF04. While the IPMF is only moderately successful in
ranking native or near native conformations, it yields the lowest mean error of 1.41 log Ki/Kd units from measured inhibition
affinities and the highest Pearson's correlation coefficient of Rp
2 0.40 for the test set. These results corroborate our initial
supposition about the role of “enriched” knowledge base. With the rapid growing volume of high-quality structural and
interaction data in the public domain, this work marks a positive step toward improving the accuracy of knowledge-based
scoring functions in binding affinity prediction.