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  Deep confidence: A computationally efficient framework for calculating reliable prediction errors for deep neural networks

Authors: Cortes-Ciriano, I. | Bender, A.

Date: 2019-04-22 |2019-04-22 |2001–1 |2019 | | | | | | |

Subject: Document Delivery | Others | Journal of Chemical Information and Modeling | Miscellaneous

Language: EN

Type: Article

Identifier: valet-20190422-103034

Description: Deep learning architectures have proved versatile in a number of drug discovery applications, including the modelling of in vitro compound activity. While controlling for prediction confidence is esse… More

  Imputation of assay bioactivity data using deep learning

Authors: Whitehead, T.M. | Hunt, P. | Segall, M.D. | Conduit, G. J. | Irwin, B.W.

Date: 2019-04-22 |2019-04-22 |2001–1 |2019 | | | | | | |

Subject: Document Delivery | Others | Journal of Chemical Information and Modeling | Miscellaneous

Language: EN

Type: Article

Identifier: valet-20190422-145232

Description: We describe a novel deep learning neural network method and its application to impute assay pIC50 values. Unlike conventional machine learning approaches, this method is trained on sparse bioactivity … More

  Predictive multitask deep neural network models for ADME-tox properties: Learning from large datasets

Authors: Wenzel, J. | Schmit, F.

Date: 2019-04-22 |2019-04-22 |2001–1 |2019 | | | | | | |

Subject: Document Delivery | Others | Journal of Chemical Information and Modeling | Miscellaneous

Language: EN

Type: Article

Identifier: valet-20190422-142243

Description: Successful drug discovery projects require control and optimization of compound properties related to pharmacokinetics, pharmacodynamics, and safety. While volume and chemotype coverage of public and … More

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