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Uncertainty quantification (UQ) is an important component of molecular property prediction, particularly for drug discovery applications where model predictions direct experimental design and where unanticipated imprecision wastes valuable time and resources.
Zhang, Y.; Lee, A. A. Bayesian semi-supervised learning for uncertainty-calibrated prediction of molecular properties and active learning. Chemical Science 2019
1902
Earlier work this paper cites.
1902
Earlier work this paper cites.
1903
Earlier work this paper cites.
1910
Earlier work this paper cites.
1912
Earlier work this paper cites.
Sheridan, R. P.; Feuston, B. P.; Maiorov, V. N.; Kearsley, S. K. Similarity to Molecules in the Training Set Is a Good Discriminator for Prediction Accuracy in QSAR. Journal of Chemical Information and Computer Sciences 2004
1928
Earlier work this paper cites.
Wilcoxon, F. Breakthroughs in statistics ; Springer, 1992; pp 196–202
1992
Earlier work this paper cites.
Nix, D.; Weigend, A. Estimating the mean and variance of the target probability distribution. Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN’94). 1994; pp 55–60 vol.1
1994
Earlier work this paper cites.
Breiman, L. Stacked regressions. Machine Learning 1996
1996
Earlier work this paper cites.
Wildman, S. A.; Crippen, G. M. Prediction of Physicochemical Parameters by Atomic Contributions. Journal of Chemical Information and Computer Sciences 1999
1999
Earlier work this paper cites.
Dietterich, T. G. Ensemble Methods in Machine Learning. International Workshop on Multiple Classifier Systems 2000
2000
Earlier work this paper cites.
2003
Earlier work this paper cites.
Landrum, G. RDKit: Open-Source Cheminformatics. 2006
2006
Earlier work this paper cites.
Demšar, J. Statistical Comparisons of Classifiers over Multiple Data Sets. Journal of Machine Learning Research 2006
2006
Cited alongside, same era.
Schroeter, T. S.; Schwaighofer, A.; Mika, S.; Ter Laak, A.; Suelzle, D.; Ganzer, U.; Heinrich, N.; Müller, K.-R. Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules. Journal of Computer-Aided Molecular Design 2007
2007
Cited alongside, same era.
Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Dubourg, V. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 2011
2011
Cited alongside, same era.
Stumpfe, D.; Bajorath, J. Exploring Activity Cliffs in Medicinal Chemistry. Journal of Medicinal Chemistry 2012
2012
Cited alongside, same era.
Kearnes, S.; McCloskey, K.; Berndl, M.; Pande, V.; Riley, P. Molecular Graph Convolutions: Moving Beyond Fingerprints. Journal of Computer-Aided Molecular Design 2016
2016
Later among the works it cites.
2017
Later among the works it cites.
2017
Later among the works it cites.
Gilmer, J.; Schoenholz, S. S.; Riley, P. F.; Vinyals, O.; Dahl, G. E. Neural Message Passing for Quantum Chemistry. Proceedings of the 34th International Conference on Machine Learning 2017
2017
Later among the works it cites.
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GPy, GPy: A Gaussian process framework in python. http://github.com/SheffieldML/GPy , since 2012
2012
Cited alongside, same era.
Cherkasov, A. et al. QSAR Modeling: Where Have You Been? Where Are You Going To? Journal of Medicinal Chemistry 2014
2014
Cited alongside, same era.
Toplak, M.; Močnik, R.; Polajnar, M.; Bosnić, Z.; Carlsson, L.; Hasselgren, C.; Demšar, J.; Boyer, S.; Zupan, B.; Stålring, J. Assessment of Machine Learning Reliability Methods for Quantifying the Applicability Domain of QSAR Regression Models. Journal of Chemical Information and Modeling 2014
2014
Cited alongside, same era.
Roy, K.; Kar, S.; Ambure, P. On a simple approach for determining applicability domain of QSAR models. Chemometrics and Intelligent Laboratory Systems 2015
2015
Cited alongside, same era.
Huang, W.; Zhao, D.; Sun, F.; Liu, H.; Chang, E. Scalable gaussian process regression using deep neural networks. Twenty-Fourth International Joint Conference on Artificial Intelligence. 2015
2015
Cited alongside, same era.
Duvenaud, D. K.; Maclaurin, D.; Iparraguirre, J.; Bombarell, R.; Hirzel, T.; Aspuru-Guzik, A.; Adams, R. P. Convolutional Networks on Graphs for Learning Molecular Fingerprints. Advances in Neural Information Processing Systems 2015
2015
Cited alongside, same era.
Gal, Y.; Ghahramani, Z. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning. Proceedings of The 33rd International Conference on Machine Learning. New York, New York, USA, 2016; pp 1050–1059
2016
Cited alongside, same era.
Aniceto, N.; Freitas, A. A.; Bender, A.; Ghafourian, T. A novel applicability domain technique for mapping predictive reliability across the chemical space of a QSAR: reliability-density neighbourhood. Journal of Cheminformatics 2016
2016
Cited alongside, same era.
Coley, C. W.; Barzilay, R.; Green, W. H.; Jaakkola, T. S.; Jensen, K. F. Convolutional Embedding of Attributed Molecular Graphs for Physical Property Prediction. Journal of Chemical Information and Modeling 2017
2017
Later among the works it cites.
Wu, Z.; Ramsundar, B.; Feinberg, E.; Gomes, J.; Geniesse, C.; Pappu, A. S.; Leswing, K.; Pande, V. MoleculeNet: A Benchmark for Molecular Machine Learning. Chemical Science 2018
2018
Later among the works it cites.
Cortés-Ciriano, I.; Bender, A. Deep Confidence: A Computationally Efficient Framework for Calculating Reliable Prediction Errors for Deep Neural Networks. Journal of Chemical Information and Modeling 2019
2019
Later among the works it cites.
Cortés-Ciriano, I.; Bender, A. Reliable Prediction Errors for Deep Neural Networks Using Test-Time Dropout. Journal of Chemical Information and Modeling 2019
2019
Later among the works it cites.
Liu, R.; Wallqvist, A. Molecular Similarity-Based Domain Applicability Metric Efficiently Identifies Out-of-Domain Compounds. Journal of Chemical Information and Modeling 2019
2019
Later among the works it cites.
Janet, J. P.; Duan, C.; Yang, T.; Nandy, A.; Kulik, H. J. A quantitative uncertainty metric controls error in neural network-driven chemical discovery. Chemical Science. 2019
2019
Later among the works it cites.
Yang, K.; Swanson, K.; Jin, W.; Coley, C.; Eiden, P.; Gao, H.; Guzman-Perez, A.; Hopper, T.; Kelley, B.; Mathea, M.; Palmer, A.; Settels, V.; Jaakkola, T.; Jensen, K.; Barzilay, R. Analyzing Learned Molecular Representations for Property Prediction. Journal of Chemical Information and Modeling 2019
2019
Later among the works it cites.
Muratov, E. N. et al. QSAR without borders. Chemical Society Reviews 2020
2020
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