Thomas Seidel

Thomas Seidel is a Professor in the Department of Pharmaceutical Sciences at the Faculty of Life Sciences. His research focuses on computational methods for drug discovery, including pharmacophore modeling, virtual screening, and ligand-receptor interaction analysis. He has contributed to the development of tools like GRADE/X-GRADE, PharmacoMatch, and QPhAR for enhancing the efficiency and accuracy of drug design processes.

His work integrates molecular dynamics simulations, machine learning, and structure-based approaches to address challenges in neuropharmacology and medicinal chemistry. Key areas include GABAA receptor modulation, metabolite prediction, and the optimization of lead compounds for drug targets such as the large neutral amino acid transporter 1 (LAT1).

Seidel’s collaborative projects span academic and industrial partnerships, with a focus on advancing predictive toxicology through initiatives like the NeuroDeRisk toolbox. His research consistently bridges computational methodologies with experimental validation, emphasizing translational applications in drug development.