A Bayesian graph convolutional network for reliable prediction of molecular properties with uncertainty quantification
暂无分享,去创建一个
[1] Alexandre Tkatchenko,et al. Quantum-chemical insights from deep tensor neural networks , 2016, Nature Communications.
[2] Alán Aspuru-Guzik,et al. The Harvard organic photovoltaic dataset , 2016, Scientific Data.
[3] Thierry Kogej,et al. Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks , 2017, ACS central science.
[4] Donald R. Jones,et al. Efficient Global Optimization of Expensive Black-Box Functions , 1998, J. Glob. Optim..
[5] George Papadatos,et al. The ChEMBL database in 2017 , 2016, Nucleic Acids Res..
[6] A. Kiureghian,et al. Aleatory or epistemic? Does it matter? , 2009 .
[7] Shumeet Baluja,et al. Advances in Neural Information Processing , 1994 .
[8] Edward O. Pyzer-Knapp,et al. A Bayesian Approach to Calibrating High-Throughput Virtual Screening Results and Application to Organic Photovoltaic Materials , 2015, 1510.00388.
[9] Mike Preuss,et al. Planning chemical syntheses with deep neural networks and symbolic AI , 2017, Nature.
[10] Christoph J. Brabec,et al. Design Rules for Donors in Bulk‐Heterojunction Solar Cells—Towards 10 % Energy‐Conversion Efficiency , 2006 .
[11] Alán Aspuru-Guzik,et al. The Harvard Clean Energy Project: Large-Scale Computational Screening and Design of Organic Photovoltaics on the World Community Grid , 2011 .
[12] D. Truhlar,et al. The M06 suite of density functionals for main group thermochemistry, thermochemical kinetics, noncovalent interactions, excited states, and transition elements: two new functionals and systematic testing of four M06-class functionals and 12 other functionals , 2008 .
[13] Gianni De Fabritiis,et al. KDEEP: Protein-Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks , 2018, J. Chem. Inf. Model..
[14] George E. Dahl,et al. Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error. , 2017, Journal of chemical theory and computation.
[15] Günter Klambauer,et al. DeepTox: Toxicity Prediction using Deep Learning , 2016, Front. Environ. Sci..
[16] Alán Aspuru-Guzik,et al. Convolutional Networks on Graphs for Learning Molecular Fingerprints , 2015, NIPS.
[17] Michael M. Mysinger,et al. Directory of Useful Decoys, Enhanced (DUD-E): Better Ligands and Decoys for Better Benchmarking , 2012, Journal of medicinal chemistry.
[18] Alán Aspuru-Guzik,et al. Phoenics: A Bayesian Optimizer for Chemistry , 2018, ACS central science.
[19] Richard N. Zare,et al. Optimizing Chemical Reactions with Deep Reinforcement Learning , 2017, ACS central science.
[20] J S Smith,et al. ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost , 2016, Chemical science.
[21] Alán Aspuru-Guzik,et al. Neural Networks for the Prediction of Organic Chemistry Reactions , 2016, ACS central science.
[22] Alán Aspuru-Guzik,et al. Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules , 2016, ACS central science.
[23] David A. Cohn,et al. Active Learning with Statistical Models , 1996, NIPS.
[24] Tanmoy Bhattacharya,et al. The need for uncertainty quantification in machine-assisted medical decision making , 2019, Nat. Mach. Intell..
[25] John K Kruschke,et al. Bayesian data analysis. , 2010, Wiley interdisciplinary reviews. Cognitive science.
[26] Arzucan Özgür,et al. DeepDTA: deep drug–target binding affinity prediction , 2018, Bioinform..
[27] Zhihai Liu,et al. Forging the Basis for Developing Protein-Ligand Interaction Scoring Functions. , 2017, Accounts of chemical research.
[28] Roberto Cipolla,et al. Concrete Problems for Autonomous Vehicle Safety: Advantages of Bayesian Deep Learning , 2017, IJCAI.
[29] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..