Miriam Mathea

15 papers A* 1Journal 13
YearRankTypeTitle / Venue / Authors
2025 J jnl
Evaluating Machine Learning Models for Molecular Property Prediction: Performance and Robustness on Out-of-Distribution Data.
J. Chem. Inf. Model.
Hosein Fooladi, Thi Ngoc Lan Vu, Miriam Mathea, Johannes Kirchmair
2024 J jnl
Augmenting optimization-based molecular design with graph neural networks.
Comput. Chem. Eng.
Shiqiang Zhang, Juan S. Campos, Christian Feldmann, Frederik Sandfort, Miriam Mathea, Ruth Misener
2024 J jnl
Limeade: Let integer molecular encoding aid.
CoRR
Shiqiang Zhang, Christian Feldmann, Frederik Sandfort, Miriam Mathea, Juan S. Campos, Ruth Misener
2024 J jnl
MolPipeline: A Python Package for Processing Molecules with RDKit in Scikit-learn.
J. Chem. Inf. Model.
Jochen Sieg, Christian Feldmann, Jennifer Hemmerich, Conrad Stork, Frederik Sandfort, Philipp Eiden, Miriam Mathea
2024 J jnl
Transformers for Molecular Property Prediction: Lessons Learned from the Past Five Years.
J. Chem. Inf. Model.
Afnan Sultan, Jochen Sieg, Miriam Mathea, Andrea Volkamer
2024 J jnl
Transformers for molecular property prediction: Lessons learned from the past five years.
CoRR
Afnan Sultan, Jochen Sieg, Miriam Mathea, Andrea Volkamer
2023 J jnl
Augmenting optimization-based molecular design with graph neural networks.
CoRR
Shiqiang Zhang, Juan S. Campos, Christian Feldmann, Frederik Sandfort, Miriam Mathea, Ruth Misener
2023 A* conf
Optimizing over trained GNNs via symmetry breaking.
NeurIPS
Shiqiang Zhang, Juan S. Campos, Christian Feldmann, David Walz, Frederik Sandfort, Miriam Mathea, Calvin Tsay, Ruth Misener
2021 J jnl
ChemBioSim: Enhancing Conformal Prediction of In Vivo Toxicity by Use of Predicted Bioactivities.
J. Chem. Inf. Model.
Marina Garcia de Lomana, Andrea Morger, Ulf Norinder, Roland Buesen, Robert Landsiedel, Andrea Volkamer, Johannes Kirchmair, Miriam Mathea
2020 J jnl
KnowTox: pipeline and case study for confident prediction of potential toxic effects of compounds in early phases of development.
J. Cheminformatics
Andrea Morger, Miriam Mathea, Janosch Achenbach, Antje Wolf, Roland Buesen, Klaus-Jürgen Schleifer, Robert Landsiedel, Andrea Volkamer
2019 J jnl
Analyzing Learned Molecular Representations for Property Prediction.
J. Chem. Inf. Model.
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
2019 J jnl
Are Learned Molecular Representations Ready For Prime Time?
CoRR
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
2019 J jnl
Correction to Analyzing Learned Molecular Representations for Property Prediction.
J. Chem. Inf. Model.
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
2018 — —
Schätzen der Klassenzugehörigkeitswahrscheinlichkeit zur Definition des Arbeitsbereichs von chemieinformatischen Klassifikationsmodellen.
Miriam Mathea
2017 J jnl
Efficiency of different measures for defining the applicability domain of classification models.
J. Cheminformatics
Waldemar Klingspohn, Miriam Mathea, Antonius ter Laak, Nikolaus Heinrich, Knut Baumann