Applying G2DE Classifier on the Energy Scoring Function Model for Molecular Docking
Date Issued
2010
Date
2010
Author(s)
Chiang, Chiun-Yao
Abstract
Research on protein-ligand interactions is a crucial part in basic biochemistry field. In this field, one of the important issues is to estimate the binding affinity between receptors and ligands. However, there is still much room for improvement in design of scoring function. In virtual screening, a good scoring function is like a strict goal keeper.
Our studies applying G2DE by using 6 features, which are Van der Waals force, Electrostatic interaction, Hydrogen bond, Desolvation, number of torsion bonds of a ligand and Ehbond, divided the 851 protein-ligand complexes dataset into several groups.
After the dataset was separated into outliers group and main group, we discovered that there are 12 complexes contains MHC_I domain was far away from the actual binding energy. By eliminating the 12, the RMSE of the predicting binding energy of the dataset is dropped from 2.12 to 2.046. We also construct an empirical scoring function according to the main group. The RMSE of the predicting binding energy of the main group RMSE is 2.006, and the R2 is 0.49.
Our paper shows the new scoring function and the outlier detection method by using G2DE, which can provide further clues in biochemistry analysis.
Subjects
energy scoring function
molecular docking
outlier detection
Type
thesis
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