Background: Drugs are very important for human life because they can provide treatment, cure,
prevention, or diagnosis of different diseases. However, they also cause side effects, which can increase
the risks for humans and pharmaceuticals companies. It is essential to identify drug side effects in drug
discovery. To date, lots of computational methods have been proposed to predict the side effects of drugs
and most of them used the fact that similar drugs always have similar side effects. However, previous
studies did not analyze which substructures are highly related to which kind of side effect.
Method: In this study, we conducted a computational investigation. In this regard, we extracted a drug set
for each side effect, which consisted of drugs having the side effect. Also, for each substructure, a set was
constructed by picking up drugs owing such substructure. The relationship between one side effect and one
substructure was evaluated based on linkages between drugs in their corresponding drug sets, resulting in
an Es value. Then, the statistical significance of Es value was measured by a permutation test.
Results and Conclusion: A number of highly related pairs of side effects and substructures were obtained
and some were extensively analyzed to confirm the reliability of the results reported in this study.