A New Quantitative Structure-Activity Relationship Model for Practical Applications using Hierarchical Clustering Genetic Algorithms
Date Issued
2004
Date
2004
Author(s)
Chen, Yen-Chih
DOI
en-US
Abstract
The purpose of quantitative structure-activity relationship (QSAR) is to formulate mathematical relationships between physico-chemical properties of compounds and their experimentally determined in vitro biological activities. The derived QSAR model can be subsequently applied to many practical applications, such as compound classification, diagnosis of drug mechanism, prediction of biological activity, and lead optimization. QSAR are commonly regarded as the best approaches to computational molecular design. To develop a reliable and versatile QSAR model, genetic algorithm-based partial least squares (GA-PLS) and hierarchical clustering-based partial least squares (HC-PLS) are employed in this thesis.
According to a series of studies, the results have been successfully validated by Selwood and Holloway data sets. The benefits of our model can be summarized as follow. First, GA-PLS is capable of selecting the significant molecular descriptors that play an important role in determining biological activity. By means of encoding the latent variable of PLS into chromosome and combining biased mutation with uniform mutation, GA-PLS can further improve the efficiency and accuracy of QSAR model. Second, HC-PLS is able to discriminate the representative compounds in the data set to facilitate molecular property prediction or to further analyze the subsets. Based on the comparison between molecular descriptors and biological activities (actual values for the training data and predicted values for the test data), the similar compounds have more potential to exhibit similar physicochemical and biological properties.
With the encouraging achievements, the highly predicted QSAR model derived by GA-PLS and HC-PLS not only enhances our understanding of the specifics of drug action, but also provides a theoretical foundation for future lead optimization.
Subjects
階層分群法
化合物篩選
遺傳演算法
電腦輔助藥物設計
生物活性預測
特徵篩選
Hierarchical Clustering
Compound Selection
Genetic Algorithm
Bioinformatics
Computer-Aided Drug Design
Biological Activity Prediction
Feature Selection
Type
thesis
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