Tony Fröhlich

The paper introduces In-Pocket, a computational method that validates and improves the chemical accuracy of protein-ligand binding interfaces using quantum mechanics and experimental data.

The paper introduces a machine learning model that learns spatial preference maps for drug-receptor interactions to guide drug discovery.

The paper introduces an algorithm for optimizing protein-ligand complex structures, improving data curation and analysis for drug discovery.

The paper introduces an Energy Decomposition Analysis to understand non-covalent binding in drug-protein complexes, validated against experimental data for PEX14-PEX5 and its inhibitors.