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Prediction of Solvent Physical Properties using the Hierarchical Clustering Method
Citation:
MARTIN, T. M. AND D. M. YOUNG. Prediction of Solvent Physical Properties using the Hierarchical Clustering Method. Presented at 2010 Fall ACS Meeting, Boston, MA, August 22 - 26, 2010.
Impact/Purpose:
To inform the public.
Description:
Recently a QSAR (Quantitative Structure Activity Relationship) method, the hierarchical clustering method, was developed to estimate acute toxicity values for large, diverse datasets. This methodology has now been applied to the estimate solvent physical properties including surface tension and the normal boiling point. The hierarchical clustering method divides a chemical dataset into a series of clusters containing similar compounds (in terms of their 2D molecular descriptors). Multilinear regression models are fit to each cluster. The toxicity or property is estimated using the prediction value from several different cluster models. The physical properties are estimated using 2D molecular structure only (i.e. w/o the use of critical constants). The hierarchical clustering methodology was able to achieve excellent predictions for the external prediction sets. A freely available software tool to estimate toxicity and physical properties has been developed. The software tool is based on the open source Chemistry Development Kit (written in Java).