Using Domain-Specific Fingerprints Generated Through Neural Networks to Enhance Ligand-Based Virtual Screening. - Janosch Menke; Oliver Koch (J. Chem. Inf. Model., 2021)

BFpack: Flexible Bayes Factor Testing of Scientific Theories in R. - Joris Mulder; Donald R. Williams; Xin Gu; Andrew Tomarken; Florian Böing-Messing; Anton Olsson-Collentine; Marlyne Meijerink; Janosch Menke; Robbie van Aert; Jean-Paul Fox; Herbert Hoijtink; Yves Rosseel; et al. (J. Stat. Softw., 2021)

Improving ligand-based virtual screening by utilizing neural networks to generate domain-specific molecular representations. - Janosch Menke (2022)

Similarity-based pairing improves efficiency of siamese neural networks for regression tasks and uncertainty quantification. - Yumeng Zhang; Janosch Menke; Jiazhen He; Eva Nittinger; Christian Tyrchan; Oliver Koch; Hongtao Zhao (J. Cheminformatics, 2023)

Metis: a python-based user interface to collect expert feedback for generative chemistry models. - Janosch Menke; Yasmine Nahal; Esben Jannik Bjerrum; Mikhail Kabeshov; Samuel Kaski; Ola Engkvist (J. Cheminformatics, 2024)

Human-in-the-loop active learning for goal-oriented molecule generation. - Yasmine Nahal; Janosch Menke; Julien Martinelli; Markus Heinonen; Mikhail Kabeshov; Jon Paul Janet; Eva Nittinger; Ola Engkvist; Samuel Kaski (J. Cheminformatics, 2024)