Background: There are a great number of tools that can be used in QSAR/QSPR studies;
they are implemented in several programs that are reviewed in this report. The usefulness of new
tools can be proved through comparison, with previously published approaches. In order to perform
the comparison, the most usual is the use of several benchmark datasets such as DRAGON and Sutherland’s
Methods: Here, an exploratory study of Atomic Weighted Vectors (AWVs), a new tool useful for
drug discovery using different datasets, is presented. In order to evaluate the performance of the
new tool, several statistics and QSAR/QSPR experiments are performed. Variability analyses are
used to quantify the information content of the AWVs obtained from the tool, by means of an information
Results: Principal components analysis is used to analyze the orthogonality of these descriptors, for
which the new MDs from AWVs provide different information from those codified by DRAGON
descriptors (0-2D). The QSAR models are obtained for every Sutherland’s dataset, according to the
original division into training/test sets, by means of the multiple linear regression with genetic algorithm
(MLR-GA). These models have been validated and compared favorably to several previously
published approaches, using the same benchmark datasets.
Conclusion: The obtained results show that this tool should be a useful strategy for the
QSAR/QSPR studies, despite its simplicity.