Knowledge-Based Scoring Functions in Drug Design: 2. Can the Knowledge Base Be Enriched?

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, including GLIDE, 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 K-i/K-d units from measured inhibition affinities and the highest Pearson's correlation coefficient of R-p(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.