Manuel S. Sellner

This paper introduces a distance-aware Transformer model that enhances ligand-based virtual screening by capturing 3D shape similarity, enabling efficient screening of large compound databases.

PanScreen assesses off-target liabilities of small molecules using structure-based modeling and deep learning, offering accurate predictions and insights into potential modes of action.

This paper evaluates dataset quality and splitting techniques for predicting protein-ligand binding affinities, introducing a tool (po-sco) and showing improved model performance with better data practices.