Target-Aware Drug Activity Model (TADAM)

Dr. Wichard began his career in biotech, later joining Schering AG’s Computational Chemistry Department, where he applied machine learning to GPCR drug discovery. He continued to develop pioneering AI models for biomedical applications, from early cancer detection at Charité Berlin to neural network-driven peptide design at the FMP, Berlin. At Bayer AG, he led innovative work in predictive toxicology, advancing in silico tools to improve safety and efficacy in drug development.

At Selvita, dr Wichard is leading the AI team in computational chemistry applying his over 20-year-experience in Artificial Intelligence and Machine Learning as an integral part of drug discovery processes and research projects of our partners.

His latest publications include innovative work on AI-driven drug discovery, such as using GANs conditioned on cell morphology for targeted drug design (Digital Discovery, 2022), a comprehensive overview of Bayer’s machine-learning-enhanced ADMET platform that spans two decades (Drug Discovery Today, 2020), and a novel approach to molecular generation based on gene expression signatures to create new, effective drug candidates (Nature Communications, 2020).

Dr. Vieth has also been deeply involved in the scientific community, organizing and chairing industry conferences and publishing over 50 peer-reviewed papers, including notable publications such as Structural Determinants of Dual Incretin Receptor Agonism by Tirzepatide (PNAS, 2022), which explores key structural features enabling dual receptor activation, and Structural Insights into Probe-Dependent Positive Allosterism of the GLP-1 Receptor (Nature Chemical Biology, 2020), Dr. Michał Vieth has significantly advanced the understanding of receptor behavior in drug design. His earlier works, such as Kinase Inhibitor Data Modeling and De Novo Inhibitor Design (J. Med. Chem., 2009) and Kinomics: Characterizing the Therapeutically Validated Kinase Space (Drug Discovery Today, 2005), have paved new pathways for drug discovery targeting kinase inhibition. Additionally, his landmark study on Grid-Based Molecular Docking in J. Comput. Chem. (2003), cited over 1,400 times, exemplifies his impactful contributions to computational chemistry. At Selvita, dr Vieth serves as a group leader of Computer Aided Drug Design.

At the European Lead Factory, Dr. Karawajczyk further honed her leadership in project management, data analysis, and strategic decision-making, successfully overseeing integrated medicinal chemistry projects targeting oncogenesis, dehydrogenase, and kinases for IPF.

Her scientific passion centers on interactions—from atomic-level electron interplay to the complex networks within proteins and the delicate balance of drug interactions within the human body. This fascination with interaction dynamics drives her commitment to advancing computational chemistry, accelerating drug discovery, and ultimately bringing innovative therapies to patients.

In 2017, Dr Karawajczyk joined Selvita, bringing in a wealth of experience in computational medicinal chemistry, and assuming the role of AI&CDD Director responsible for the integration of AI throughout all drug discovery processes and research projects.