MELLODDY: Cross-pharma Federated Learning at Unprecedented Scale Unlocks Benefits in QSAR without Compromising Proprietary Information - Wouter Heyndrickx; Lewis H. Mervin; Tobias Morawietz; Noé Sturm; Lukas Friedrich; Adam Zalewski; Anastasia Pentina; Lina Humbeck; Martijn Oldenhof; Ritsuya Niwayama; Peter Schmidtke; Nikolas Fechner; et al. (J. Chem. Inf. Model., 2024)

Industry-Scale Orchestrated Federated Learning for Drug Discovery - Martijn Oldenhof; Gergely Ács; Balázs Pejó; Ansgar Schuffenhauer; Nicholas Holway; Noé Sturm; Arne Dieckmann; Oliver Fortmeier; Eric Boniface; Clément Mayer; Arnaud Gohier; Peter Schmidtke; et al. (Thirty-Seventh AAAI Conference on Artificial Intelligence, AAAI 2023, Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence, IAAI 2023, Thirteenth Symposium on Educational Advances in Artificial Intelligence, EAAI 2023, Washington, DC, USA, February 7-14, 2023, 2023)

Industry-Scale Orchestrated Federated Learning for Drug Discovery - Martijn Oldenhof; Gergely Ács; Balázs Pejó; Ansgar Schuffenhauer; Nicholas Holway; Noé Sturm; Arne Dieckmann; Oliver Fortmeier; Eric Boniface; Clément Mayer; Arnaud Gohier; Peter Schmidtke; et al. (CoRR, 2022)

Splitting chemical structure data sets for federated privacy-preserving machine learning - Jaak Simm; Lina Humbeck; Adam Zalewski; Noé Sturm; Wouter Heyndrickx; Yves Moreau; Bernd Beck; Ansgar Schuffenhauer (J. Cheminformatics, 2021)

Comparison of Chemical Structure and Cell Morphology Information for Multitask Bioactivity Predictions - Maria-Anna Trapotsi; Lewis H. Mervin; Avid M. Afzal; Noé Sturm; Ola Engkvist; Ian P. Barrett; Andreas Bender (J. Chem. Inf. Model., 2021)

Industry-scale application and evaluation of deep learning for drug target prediction - Noé Sturm; Andreas Mayr; Thanh Le Van; Vladimir I. Chupakhin; Hugo Ceulemans; Jörg K. Wegner; José Felipe Golib Dzib; Nina Jeliazkova; Yves Vandriessche; Stanislav Böhm; Vojtech Cima; Jan Martinovic; et al. (J. Cheminformatics, 2020)

Combining structural and bioactivity-based fingerprints improves prediction performance and scaffold hopping capability - Oliver Laufkötter; Noé Sturm; Jürgen Bajorath; Hongming Chen; Ola Engkvist (J. Cheminformatics, 2019)

Application of Bioactivity Profile-Based Fingerprints for Building Machine Learning Models - Noé Sturm; Jiangming Sun; Yves Vandriessche; Andreas Mayr; Günter Klambauer; Lars Carlsson; Ola Engkvist; Hongming Chen (J. Chem. Inf. Model., 2019)

Exploration and Comparison of the Geometrical and Physicochemical Properties of an \(α\)C Allosteric Pocket in the Structural Kinome - Noé Sturm; Annachiara Tinivella; Giulio Rastelli (J. Chem. Inf. Model., 2018)

Structural Insights into the Molecular Basis of the Ligand Promiscuity - Noé Sturm; Jérémy Desaphy; Ronald J. Quinn; Didier Rognan; Esther Kellenberger (J. Chem. Inf. Model., 2012)