Background: Drugs are very important for human life because they can provide treatment, cure, prevention, or diagnosis of different diseases. However, they also bring side effects, which can give great risks for human bodies 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 side effect.
Method: In this study, we did a computational investigation in this regard. To do that, we extracted a drug set for each side effect, which consisted of drugs having such effect side. 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: Lots 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.