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Uncertainty quantification of molecular property prediction using Bayesian neural network models.

, , and . CoRR, (2019)

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Predicting Drug-Target Interaction Using a Novel Graph Neural Network with 3D Structure-Embedded Graph Representation., , , , , and . J. Chem. Inf. Model., 59 (9): 3981-3988 (2019)Uncertainty quantification of molecular property prediction using Bayesian neural network models., , and . CoRR, (2019)Molecular generative model based on conditional variational autoencoder for de novo molecular design., , , and . J. Cheminformatics, 10 (1): 31:1-31:9 (2018)Hit and Lead Discovery with Explorative RL and Fragment-based Molecule Generation., , , , and . NeurIPS, page 7924-7936. (2021)Uncertainty quantification of molecular property prediction with Bayesian neural networks., , and . CoRR, (2019)A comprehensive study on the prediction reliability of graph neural networks for virtual screening., , and . CoRR, (2020)Comprehensive Study on Molecular Supervised Learning with Graph Neural Networks., , , , , , , and . J. Chem. Inf. Model., 60 (12): 5936-5945 (2020)Predicting drug-target interaction using 3D structure-embedded graph representations from graph neural networks., , , , , and . CoRR, (2019)Molecular Generative Model Based On Adversarially Regularized Autoencoder., , , and . CoRR, (2019)Understanding active learning of molecular docking and its applications., , and . CoRR, (2024)