Many different measures of structural similarity have been suggested for matching chemical structures, each such measure focusing upon some particular type of molecular characteristic. The multi-faceted nature of biological activity suggests that an appropriate similarity measure should encompass many different types of characteristic, and this paper discusses the use of data fusion methods to combine the results of searches based on multiple similarity measures. Experiments with several different types of dataset and activity suggest that data fusion provides a simple, but effective, approach to the combination of individual similarity measures. The best results were generally obtained with a fusion rule that sums the rank positions achieved by each molecule in searches using individual measures.
Many different measures of structural similarity have been suggested for matching chemical structures, each such measure focusing upon some particular type of molecular characteristic. The multi-faceted nature of biological activity suggests that an appropriate similarity measure should encompass many different types of characteristic, and this paper discusses the use of data fusion methods to combine the results of searches based on multiple similarity measures. Experiments with several different types of dataset and activity suggest that data fusion provides a simple, but effective, approach to the combination of individual similarity measures. The best results were generally obtained with a fusion rule that sums the rank positions achieved by each molecule in searches using individual measures.
EVA is a new molecular descriptor that provides a concise summary
of the fundamental frequency components
of a molecule's infrared range vibrational spectrum in a vector
format. Target structures from the Starlist
database are used to demonstrate the effectiveness of the descriptor
for similarity searching and its difference
from a conventional similarity measure based on the matching of
two-dimensional (2D) fingerprints. The
use of data fusion on the rankings resulting from the EVA-based and the
2D-based similarity measures
results in a combined ranking that can be more effective in simulated
property prediction experiments than
either of the individual rankings.
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