A knowledge-guided strategy for improving the accuracy of scoring functions in binding affinity prediction

Background: Current scoring functions are not very successful in protein-ligand binding affinity prediction albeit their popularity in structure-based drug designs. Here, we propose a general knowledge-guided scoring (KGS) strategy to tackle this problem. Our KGS strategy computes the binding constant of a given protein-ligand complex based on the known binding constant of an appropriate reference complex. A good training set that includes a sufficient number of protein-ligand complexes with known binding data needs to be supplied for finding the reference complex. The reference complex is required to share a similar pattern of key protein-ligand interactions to that of the complex of interest. Thus, some uncertain factors in protein-ligand binding may cancel out, resulting in a more accurate prediction of absolute binding constants. Results: In our study, an automatic algorithm was developed for summarizing key protein-ligand interactions as a pharmacophore model and identifying the ...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Tiejun Cheng, Zhihai Liu, Renxiao Wang
• Keywords: Binding Constants, BMCBI 2010, KGS Strategy, Reference Complex