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

Industry-Scale Orchestrated Federated Learning for Drug Discovery. - Oldenhof, Martijn; Ács, Gergely; Pejó, Balázs; Schuffenhauer, Ansgar; Holway, Nicholas; Sturm, Noé; Dieckmann, Arne; Fortmeier, Oliver; Boniface, Eric; Mayer, Clément; Gohier, Arnaud; Schmidtke, Peter; et al. (2023)

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

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

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

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

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

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

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

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