Benefit of Retraining pKa Models Studied Using Internally Measured Data. - Peter Gedeck; Yipin Lu; Suzanne Skolnik; Stephane Rodde; Gavin Dollinger; Weiping Jia; Giuliano Berellini; Riccardo Vianello; Bernard Faller; Franco Lombardo (J. Chem. Inf. Model., 2015)

Developing Collaborative QSAR Models Without Sharing Structures. - Peter Gedeck; Suzanne Skolnik; Stephane Rodde (J. Chem. Inf. Model., 2017)

High accuracy quantum-chemistry-based calculation and blind prediction of macroscopic pKa values in the context of the SAMPL6 challenge. - Philipp Pracht; Rainer Wilcken; Anikó Udvarhelyi; Stephane Rodde; Stefan Grimme (J. Comput. Aided Mol. Des., 2018)

Prediction of pKa Using Machine Learning Methods with Rooted Topological Torsion Fingerprints: Application to Aliphatic Amines. - Yipin Lu; Shankara Anand; William A. Shirley; Peter Gedeck; Brian P. Kelley; Suzanne Skolnik; Stephane Rodde; Mai Nguyen; Mika K. Lindvall; Weiping Jia (J. Chem. Inf. Model., 2019)

ReSCoSS: a flexible quantum chemistry workflow identifying relevant solution conformers of drug-like molecules. - Anikó Udvarhelyi; Stephane Rodde; Rainer Wilcken (J. Comput. Aided Mol. Des., 2021)

Reactivities of acrylamide warheads toward cysteine targets: a QM/ML approach to covalent inhibitor design. - Aaron D. Danilack; Callum J. Dickson; Cihan Soylu; Mike Fortunato; Stephane Rodde; Hagen Münkler; Viktor Hornak; José S. Duca (J. Comput. Aided Mol. Des., 2024)