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BMCBI
2010

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

13 years 11 months ago
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 ...
Tiejun Cheng, Zhihai Liu, Renxiao Wang
Added 08 Dec 2010
Updated 08 Dec 2010
Type Journal
Year 2010
Where BMCBI
Authors Tiejun Cheng, Zhihai Liu, Renxiao Wang
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