Multilabel and Missing Label Methods for Binary Quantitative Structure–Activity Relationship Models: An Application for the Prediction of Adverse Drug Reactions

The prediction of
adverse drug reactions in the discovery of new
medicines is highly challenging. In the task of predicting the adverse
reactions of chemical compounds, information about different targets
is often available. Although we can focus on every adverse drug reaction
prediction separately, multilabel approaches have been proven useful
in many research areas for taking advantage of the relationship among
the targets. However, when approaching the prediction problem from
a multilabel point of view, we have to deal with the lack of information
for some labels. This missing labels problem is a relevant issue in
the field of cheminformatics approaches. This paper aims to predict
the adverse drug reaction of commercial drugs using a multilabel approach
where the possible presence of missing labels is also taken into consideration.
We propose the use of multilabel methods to deal with the prediction
of a large set of 27 different adverse reaction targets. We also propose
the use of multilabel methods specifically designed to deal with the
missing labels problem to test their ability to solve this difficult
problem. The results show the validity of the proposed approach, demonstrating
a superior performance of the multilabel method compared with the
single-label approach in addressing the problem of adverse drug reaction
prediction.