Evaluating point-prediction uncertainties in neural networks for protein-ligand binding prediction
[1];
[3];
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States). Center for Applied Scientific Computing
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States). Biological Science and Security Center
- Frederick National Laboratory for Cancer Research, Frederick, MD (United States)
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Neural Network (NN) models provide potential to speed up the drug discovery process and reduce its failure rates. The success of NN models requires uncertainty quantification (UQ) as drug discovery explores chemical space beyond the training data distribution. Standard NN models do not provide uncertainty information. Some methods require changing the NN architecture or training procedure, limiting the selection of NN models. Moreover, predictive uncertainty can come from different sources. It is important to have the ability to separately model different types of predictive uncertainty, as the model can take assorted actions depending on the source of uncertainty. In this paper, we examine UQ methods that estimate different sources of predictive uncertainty for NN models aiming at protein-ligand binding prediction. We use our prior knowledge on chemical compounds to design the experiments. By utilizing a visualization method we create non-overlapping and chemically diverse partitions from a collection of chemical compounds. These partitions are used as training and test set splits to explore NN model uncertainty. We demonstrate how the uncertainties estimated by the selected methods describe different sources of uncertainty under different partitions and featurization schemes and the relationship to prediction error.
- Research Organization:
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA); Defense Threat Reduction Agency (DTRA); National Institutes of Health (NIH); Department of Health and Human Services
- Grant/Contract Number:
- AC52-07NA27344; HDTRA1036045; 75N91019D00024; 75N91019F00134
- OSTI ID:
- 1988215
- Report Number(s):
- LLNL-JRNL-839676; 1060646
- Journal Information:
- Artificial Intelligence Chemistry, Vol. 1, Issue 1; ISSN 2949-7477
- Publisher:
- ElsevierCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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