Protein-based virtual screening plays an important role in modern drug discovery
process. Most protein-based virtual screening experiments are carried out by docking
programs. The accuracy of a docking program highly relies on the incorporated scoring
function based on various energy terms. The existing scoring functions deal all the
energy terms with equal weight or other weight function derived by physical
characteristics. These existing scoring functions are not protein-dependent. We
expect that a protein-specific scoring function, which can reflect the protein
characteristics, may improve the docking results. Therefore, we propose a protein-
specific rescoring approach to select potential ligands by adjusting the weights of
energy terms. The protein-specific scoring function is based on the linear regression
analysis associated with an outlier detection approach. The scoring function
incorporated in DOCK program is used as the model system. The performance of our
method was evaluated by the DUD docked data set which contains 40 protein targets.
The results show that this method can improve the enrichment factors for all the 40
protein targets. We further expend the protein-specific scoring function to a larger
database and the results also show significant improvement. Our method does not limit
to improve DOCK scoring function. It can be adopted to improve other programs such as
GOLD and Glide programs. We believe that this method can be applied to virtual screening
experiments and elevates the hits rate significantly, which can be beneficial to modern
drug discovery process.