Evolutionary Game Theory by Jörgen Weibull. - Daniel Probst (J. Artif. Soc. Soc. Simul., 1999)

Learning by matching patterns. - Val E. Lambson; Daniel Probst (Games Econ. Behav., 2004)

An Early-Warning System to Support Activities in the Management of Customer Equity and How to Obtain the Most from Spatial Customer Equity Potentials. - Klaus Thiel; Daniel Probst (GfKl, 2007)

WebMolCS: A Web-Based Interface for Visualizing Molecules in Three-Dimensional Chemical Spaces. - Mahendra Awale; Daniel Probst; Jean-Louis Reymond (J. Chem. Inf. Model., 2017)

FUn: a framework for interactive visualizations of large, high-dimensional datasets on the web. - Daniel Probst; Jean-Louis Reymond (Bioinform., 2018)

A probabilistic molecular fingerprint for big data settings. - Daniel Probst; Jean-Louis Reymond (J. Cheminformatics, 2018)

SmilesDrawer: Parsing and Drawing SMILES-Encoded Molecular Structures Using Client-Side JavaScript. - Daniel Probst; Jean-Louis Reymond (J. Chem. Inf. Model., 2018)

Exploring DrugBank in Virtual Reality Chemical Space. - Daniel Probst; Jean-Louis Reymond (J. Chem. Inf. Model., 2018)

One molecular fingerprint to rule them all: drugs, biomolecules, and the metabolome. - Alice Capecchi; Daniel Probst; Jean-Louis Reymond (J. Cheminformatics, 2020)

Visualization of very large high-dimensional data sets as minimum spanning trees. - Daniel Probst; Jean-Louis Reymond (J. Cheminformatics, 2020)

An automated machine learning-genetic algorithm (AutoML-GA) approach for efficient simulation-driven engine design optimization. - Opeoluwa Owoyele; Pinaki Pal; Alvaro Vidal Torreira; Daniel Probst; Matthew Shaxted; Michael Wilde; Peter Kelly Senecal (CoRR, 2021)

Mapping the space of chemical reactions using attention-based neural networks. - Philippe Schwaller; Daniel Probst; Alain C. Vaucher; Vishnu H. Nair; David Kreutter; Teodoro Laino; Jean-Louis Reymond (Nat. Mach. Intell., 2021)

Social and environmental impact of recent developments in machine learning on biology and chemistry research. - Daniel Probst (CoRR, 2022)

An explainability framework for deep learning on chemical reactions exemplified by enzyme-catalysed reaction classification. - Daniel Probst (J. Cheminformatics, 2023)

Molecular set representation learning. - Maria Boulougouri; Pierre Vandergheynst; Daniel Probst (Nat. Mac. Intell., 2024)

Implicit Neural Representations of Molecular Vector-Valued Functions. - Jirka Lhotka; Daniel Probst (CoRR, 2025)

To Bin or not to Bin: Alternative Representations of Mass Spectra. - Niek F. de Jonge; Justin J. J. van der Hooft; Daniel Probst (CoRR, 2025)

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings. - Aditya Sengar; Ali Hariri; Daniel Probst; Patrick Barth; Pierre Vandergheynst (CoRR, 2025)