Miriam Mathea

According to our database, Miriam Mathea authored at least 9 papers between 2017 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

Augmenting optimization-based molecular design with graph neural networks.

Shiqiang Zhang, Juan S. Campos, Christian Feldmann, Frederik Sandfort, Miriam Mathea, Ruth Misener

CoRR, 2023

Optimizing over trained GNNs via symmetry breaking.

Shiqiang Zhang, Juan S. Campos, Christian Feldmann, David Walz, Frederik Sandfort, Miriam Mathea, Calvin Tsay, Ruth Misener

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2021

ChemBioSim: Enhancing Conformal Prediction of In Vivo Toxicity by Use of Predicted Bioactivities.

Marina Garcia de Lomana, Andrea Morger, Ulf Norinder, Roland Buesen, Robert Landsiedel, Andrea Volkamer, Johannes Kirchmair, Miriam Mathea

J. Chem. Inf. Model., 2021

2020

KnowTox: pipeline and case study for confident prediction of potential toxic effects of compounds in early phases of development.

Andrea Morger, Miriam Mathea, Janosch Achenbach, Antje Wolf, Roland Buesen, Klaus-Jürgen Schleifer, Robert Landsiedel, Andrea Volkamer

J. Cheminformatics, 2020

2019

Correction to Analyzing Learned Molecular Representations for Property Prediction.

Kevin Yang, Kyle Swanson, Wengong Jin, Connor W. Coley, Philipp Eiden, Hua Gao, Angel Guzman-Perez, Timothy Hopper, Brian Kelley, Miriam Mathea, Andrew Palmer, Volker Settels, Tommi S. Jaakkola, Klavs F. Jensen, Regina Barzilay

J. Chem. Inf. Model., 2019

Analyzing Learned Molecular Representations for Property Prediction.

Kevin Yang, Kyle Swanson, Wengong Jin, Connor W. Coley, Philipp Eiden, Hua Gao, Angel Guzman-Perez, Timothy Hopper, Brian Kelley, Miriam Mathea, Andrew Palmer, Volker Settels, Tommi S. Jaakkola, Klavs F. Jensen, Regina Barzilay

J. Chem. Inf. Model., 2019

Are Learned Molecular Representations Ready For Prime Time?

Kevin Yang, Kyle Swanson, Wengong Jin, Connor W. Coley, Philipp Eiden, Hua Gao, Angel Guzman-Perez, Timothy Hopper, Brian Kelley, Miriam Mathea, Andrew Palmer, Volker Settels, Tommi S. Jaakkola, Klavs F. Jensen, Regina Barzilay

CoRR, 2019

2018

Schätzen der Klassenzugehörigkeitswahrscheinlichkeit zur Definition des Arbeitsbereichs von chemieinformatischen Klassifikationsmodellen.

Miriam Mathea

PhD thesis, 2018

2017

Efficiency of different measures for defining the applicability domain of classification models.

Waldemar Klingspohn, Miriam Mathea, Antonius ter Laak, Nikolaus Heinrich, Knut Baumann

J. Cheminformatics, 2017