MODLAB

The molecular design cycle involves multiple scientific disciplines and requires trans-disciplinary thinking. We employ a broad repertoire of machine learning methods and bio/cheminformatics techniques for automated hypothesis generation, activity prediction, and validation.

In tight combination with chemical synthesis and biochemical activity testing, computer-generated hypotheses and methods are assessed for their practical applicability in medicinal chemistry. At the heart of our studies lies the machine-driven de novo design of small molecules and peptides with the desired pharmacological activity.

Our research includes drug re-purposing, target profile prediction and drug polypharmacology, the analysis of protein structure and modulation of protein-protein interaction, virtual compound screening, the design of natural product analogs, peptides, and the de-orphanizing of drugs and their macromolecular receptors.

We run own state-of-the-art chemical synthesis and biophysical testing facilities.