Comparing 3D Pharmacophore Triplets and 2D Fingerprints for Selecting Diverse Compound Subsets. - Hans Matter; Thorsten Pötter (J. Chem. Inf. Comput. Sci., 1999)

3D-QSAR Based on Quantum-Chemical Molecular Fields: Toward an Improved Description of Halogen Interactions. - Stefan Güssregen; Hans Matter; Gerhard Hessler; Marco Müller 002; K. Friedemann Schmidt; Timothy Clark (J. Chem. Inf. Model., 2012)

Quantum Mechanics-Based Properties for 3D-QSAR. - Ahmed M. El Kerdawy; Stefan Güssregen; Hans Matter; Matthias Hennemann; Timothy Clark (J. Chem. Inf. Model., 2013)

Quantum-mechanics-based molecular interaction fields for 3D-QSAR. - Ahmed M. El Kerdawy; Stefan Güssregen; Hans Matter; Matthias Hennemann; Timothy Clark (J. Cheminformatics, 2014)

Thermodynamic Characterization of Hydration Sites from Integral Equation-Derived Free Energy Densities: Application to Protein Binding Sites and Ligand Series. - Stefan Güssregen; Hans Matter; Gerhard Hessler; Evanthia Lionta; Jochen Heil; Stefan M. Kast (J. Chem. Inf. Model., 2017)

Predictive Multitask Deep Neural Network Models for ADME-Tox Properties: Learning from Large Data Sets. - Jan Wenzel; Hans Matter; K. Friedemann Schmidt (J. Chem. Inf. Model., 2019)

Impact of Different Automated Binding Pose Generation Approaches on Relative Binding Free Energy Simulations. - Daniel Cappel; Steven Jerome; Gerhard Hessler; Hans Matter (J. Chem. Inf. Model., 2020)

Using Graph Databases to Investigate Trends in Structure-Activity Relationship Networks. - Hans Matter; Christian Buning; Dan Dragos Stefanescu; Sven Ruf; Gerhard Hessler (J. Chem. Inf. Model., 2020)

Interpretation of Structure-Activity Relationships in Real-World Drug Design Data Sets Using Explainable Artificial Intelligence. - Tobias Harren; Hans Matter; Gerhard Hessler; Matthias Rarey; Christoph Grebner (J. Chem. Inf. Model., 2022)

Integrating Reaction Schemes, Reagent Databases, and Virtual Libraries into Fragment-Based Design by Reinforcement Learning. - Susanne Sauer; Hans Matter; Gerhard Hessler; Christoph Grebner (J. Chem. Inf. Model., 2023)

Many-Shot In-Context Learning for Molecular Inverse Design. - Saeed Moayedpour; Alejandro Corrochano-Navarro; Faryad Sahneh; Shahriar Noroozizadeh; Alexander Koetter; Jiri Vymetal; Lorenzo Kogler-Anele; Pablo Mas; Yasser Jangjou; Sizhen Li; Michael Bailey; Marc Bianciotto; et al. (CoRR, 2024)