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)

Schätzen der Klassenzugehörigkeitswahrscheinlichkeit zur Definition des Arbeitsbereichs von chemieinformatischen Klassifikationsmodellen. - Miriam Mathea (Cuvillier Verlag, 2018)

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; et al. (CoRR, 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; et al. (J. Chem. Inf. Model., 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; et al. (J. Chem. Inf. Model., 2019)

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)

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)

Optimizing over trained GNNs via symmetry breaking. - Shiqiang Zhang; Juan S. Campos; Christian Feldmann; David Walz; Frederik Sandfort; Miriam Mathea; Calvin Tsay; Ruth Misener (NeurIPS, 2023)

Augmenting optimization-based molecular design with graph neural networks. - Shiqiang Zhang; Juan S. Campos; Christian Feldmann; Frederik Sandfort; Miriam Mathea; Ruth Misener (Comput. Chem. Eng., 2024)

Limeade: Let integer molecular encoding aid. - Shiqiang Zhang; Christian Feldmann; Frederik Sandfort; Miriam Mathea; Juan S. Campos; Ruth Misener (CoRR, 2024)

MolPipeline: A Python Package for Processing Molecules with RDKit in Scikit-learn. - Jochen Sieg; Christian Feldmann; Jennifer Hemmerich; Conrad Stork; Frederik Sandfort; Philipp Eiden; Miriam Mathea (J. Chem. Inf. Model., 2024)

Transformers for Molecular Property Prediction: Lessons Learned from the Past Five Years. - Afnan Sultan; Jochen Sieg; Miriam Mathea; Andrea Volkamer (J. Chem. Inf. Model., 2024)