  While the use of deep learning in drug discovery is gaining increasing
attention, the lack of methods to compute reliable errors in prediction for
Neural Networks prevents their application to guide decision making in domains
where identifying unreliable predictions is essential, e.g. precision medicine.
Here, we present a framework to compute reliable errors in prediction for
Neural Networks using Test-Time Dropout and Conformal Prediction. Specifically,
the algorithm consists of training a single Neural Network using dropout, and
then applying it N times to both the validation and test sets, also employing
dropout in this step. Therefore, for each instance in the validation and test
sets an ensemble of predictions were generated. The residuals and absolute
errors in prediction for the validation set were then used to compute
prediction errors for test set instances using Conformal Prediction. We show
using 24 bioactivity data sets from ChEMBL 23 that dropout Conformal Predictors
are valid (i.e., the fraction of instances whose true value lies within the
predicted interval strongly correlates with the confidence level) and
efficient, as the predicted confidence intervals span a narrower set of values
than those computed with Conformal Predictors generated using Random Forest
(RF) models. Lastly, we show in retrospective virtual screening experiments
that dropout and RF-based Conformal Predictors lead to comparable retrieval
rates of active compounds. Overall, we propose a computationally efficient
framework (as only N extra forward passes are required in addition to training
a single network) to harness Test-Time Dropout and the Conformal Prediction
framework, and to thereby generate reliable prediction errors for deep Neural
Networks.
