Using Predicted Bioactivity Profiles to Improve Predictive Modeling

Predictive modeling
is a cornerstone
in early drug development. Using information for multiple domains
or across prediction tasks has the potential to improve the performance
of predictive modeling. However, aggregating data often leads to incomplete
data matrices that might be limiting for modeling. In line with previous
studies, we show that by generating predicted bioactivity profiles,
and using these as additional features, prediction accuracy of biological
endpoints can be improved. Using conformal prediction, a type of confidence
predictor, we present a robust framework for the calculation of these
profiles and the evaluation of their impact. We report on the outcomes
from several approaches to generate the predicted profiles on 16 datasets
in cytotoxicity and bioactivity and show that efficiency is improved
the most when including the p-values from conformal
prediction as bioactivity profiles.