Data Resources for the Computer-Guided Discovery of Bioactive Natural Products. - Ya Chen 002; Christina de Bruyn Kops; Johannes Kirchmair (J. Chem. Inf. Model., 2017)

Characterization of the Chemical Space of Known and Readily Obtainable Natural Products. - Ya Chen 002; Marina Garcia de Lomana; Nils-Ole Friedrich; Johannes Kirchmair (J. Chem. Inf. Model., 2018)

Hit Dexter 2.0: Machine-Learning Models for the Prediction of Frequent Hitters. - Conrad Stork; Ya Chen 002; Martin Sícho; Johannes Kirchmair (J. Chem. Inf. Model., 2019)

Validation strategies for target prediction methods. - Neann Mathai; Ya Chen 002; Johannes Kirchmair (Briefings Bioinform., 2020)

NERDD: a web portal providing access to in silico tools for drug discovery. - Conrad Stork; Gerd Embruch; Martin Sícho; Christina de Bruyn Kops; Ya Chen 002; Daniel Svozil; Johannes Kirchmair (Bioinform., 2020)

Scope of 3D Shape-Based Approaches in Predicting the Macromolecular Targets of Structurally Complex Small Molecules Including Natural Products and Macrocyclic Ligands. - Ya Chen 002; Neann Mathai; Johannes Kirchmair (J. Chem. Inf. Model., 2020)

Cheminformatics in Natural Product-Based Drug Discovery - Ya Chen 002 (2020)

Active Learning Approach for Guiding Site-of-Metabolism Measurement and Annotation. - Ya Chen 002; Thomas Seidel; Roxane Axel Jacob; Steffen Hirte; Angelica Mazzolari; Alessandro Pedretti; Giulio Vistoli; Thierry Langer; Filip Miljkovic; Johannes Kirchmair (J. Chem. Inf. Model., 2024)

Site-of-Metabolism Prediction with Aleatoric and Epistemic Uncertainty Quantification. - Roxane Axel Jacob; Oliver Wieder; Ya Chen 002; Angelica Mazzolari; Andreas Bergner; Klaus-Jürgen Schleifer; Johannes Kirchmair (J. Chem. Inf. Model., 2025)

Integrated Virtual Screening Approach Identifies New CYP19A1 Inhibitors. - Sijie Liu; Jie Wu; Ya Chen 002; Clemens Alexander Wolf; Matthias Bureik; Johannes Kirchmair; Mario Andrea Marchisio; Gerhard Wolber (J. Chem. Inf. Model., 2025)