Increasing the prediction confidence using deep ensemble learning. Deep learning models suffer from a decrease in performance when applied to out-of-domain data , a well-known issue in quantitative structure-activity relationship modeling . To increase the confidence in the bioactivity predictions, we used a deep ensemble model by combining the predictions of multiple models with a majority voting approach . Owing to the nondeterministic optimization process, repeats of the same CLM training procedure will lead to different models. Deep ensemble learning has been shown to perform well across different domains to account for the predictive uncertainty of the models, while having the benefit of being straightforward to implement . Accordingly, 100 different E-CLM classifiers were trained on the bioactivity prediction task. The level of confidence in a prediction was defined as the number of models that classified a given input molecule as "highly active".