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A Hierarchical Clustering Methodology for the Estimation of Toxicity
Citation:
MARTIN, T. M., P. F. HARTEN, R. Venkataphy, S. Das, AND D. M. YOUNG. A Hierarchical Clustering Methodology for the Estimation of Toxicity. DOI: 10.1080/1537651, R. Dixit, L.G. Valerio, C. Palmeira, D. Ghosh (ed.), Toxicology Mechanisms and Methods. Taylor & Francis, Inc., Philadelphia, PA, 18(2):251-266, (2008).
Impact/Purpose:
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Description:
A Quantitative Structure Activity Relationship (QSAR) methodology based on hierarchical clustering was developed to predict toxicological endpoints. This methodology utilizes Ward's method to divide a training set into a series of structurally similar clusters. The structural similarity is defined in terms of 2-D physicochemical descriptors (such as connectivity and E-state indices). A genetic algorithm based technique is used to generate statistically valid quantitative structre-activity relationships 9QSAR) models for each cluster (using the pool of descriptors described above). The toxicity for a given query compound is estimated using the weighted average of the predictions from the closest cluster from each step in the hierarchical clustering assuming that the compound is within the domain of applicability of the cluster. The hierarchical clustering methodology was tested using the Tetrahymena pyriformis data set containing 644 chemicals in the training set and with two prediction sets containing 339 and 110 chemicals. The results for the h ierarchical clustering methodology were compared to the results from several different QSAR methodologies.
