Accurate prediction of ligand-binding poses is crucial for understanding 
molecular interactions and is very important for drug discovery, structural 
design, and optimization. In this study, we developed a novel scoring program, 
HotLig, which applies the Connolly surface of a protein to calculate hydrophobic 
interaction and paired pharmacophore interactions with ligands. In addition to 
molecular surface distance, ligand-contacting areas and hydrogen-bond angles 
were also introduced to the scoring functions in HotLig. Four individual energy 
scoring functions for H-bonds, ionic pairs, metal coordination, and hydrophobic 
effects were derived from 600 protein-ligand complexes, and then, their 
weighting factors were optimized through an interaction-characterized training 
set. Success rates of ligand-binding-pose predictions (with a root mean squared 
deviation of ≤2 Å) for the Wang, GOLD, and Cheng data sets were respectively 
validated to be 91.0%, 87.0%, and 85.6%. HotLig was found to possess equally 
good predictive powers for the hydrophilic (88.6%) and hydrophobic subsets 
(87.5%), and the success rate for the mixed subset was as high as 96.9%. The 
Spearman correlation coefficients were as good as 0.609 to 0.668, which 
indicates HotLig also has satisfactory predictive power for binding affinities. 
These results suggested that the HotLig can analyze diverse ligands, including 
peptides, and is expected to be a powerful tool for drug design and discovery.