Stefan Günther

This study uses computational methods to stabilize Plasmodium falciparum proteins, enabling reliable biophysical analysis and drug discovery.

This study identifies a potent inhibitor of PfBDP1 in Plasmodium falciparum through virtual docking, calorimetry, and crystallography, showing parasite sensitivity and potential as a malaria therapeutic.

This paper presents a method to predict epitope activity of non-peptidic compounds using machine learning and the IEDB database, achieving ROC-AUC values between 0.69-0.96.

The paper develops a benchmark dataset and two machine learning models to classify compound-protein relationships in scientific abstracts as functional or non-functional.