| Summary: | 博士 === 國立臺灣大學 === 醫學工程學研究所 === 100 === The scoring functions for protein-ligand interactions plays central roles in computational drug design, virtual screening of chemical libraries for new lead identification, and prediction of possible binding targets of small chemical molecules. We have developed the robust scoring functions and applied it for computational drug design. Ordinary least-squares (OLS) regression has been used widely for constructing the scoring functions for protein-ligand interactions. However, OLS is very sensitive to the existence of outliers, and models constructed using that are easily affected by the outliers or even the choice of the data set. On the other hand, determination of atomic charges is regarded as of central importance, because the electrostatic interaction is known to be a key contributing factor for biomolecular association. Our new scoring functions were based on the functional form of the AutoDock4 scoring function and using more rigorous charge models derived from quantum mechanics and molecular mechanics. On top of that, we developed a protocol for calibrating the robust scoring function by using the robust regression analysis. In another word, the problem of outliers in the training set can be solved. The assessments show that our new scoring functions outperformed most of other scoring functions on predicting binding affinity and discriminating the native pose from decoys.
In the first chapter of the present dissertation, we will explore the foundations of different classes of scoring functions, their possible limitations, and their suitable application domains. The second chapter introduces the docking program AutoDock which is the basis of this study. In Chapter 3 and 4, the development of the robust scoring functions and its assessments will be described, respectively. In Chapter 5, a novel application of web service, namely idTarget, which aims to identify protein targets, will be presented.
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