Postdoctoral Position in AI-Driven Drug Design

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Postdoctoral Position in AI-Driven Drug Design

Artificial intelligence is rapidly transforming molecular design and drug discovery. However, the identification of successful drug candidates requires more than generating molecules with high predicted affinity: selectivity, physicochemical properties, potential adverse effects, synthetic accessibility, and experimental feedback must be considered simultaneously.

Our research in the Computational Pharmacy group at the University of Basel focuses on developing next-generation AI approaches for drug design by combining state-of-the-art machine learning with physicochemical knowledge and molecular modeling. Representative publications from our group include:

Your position A fully funded Postdoctoral position is available in the Computational Pharmacy group at the University of Basel within an international Innosuisse research project on AI-driven closed-loop drug discovery. The project aims to establish an integrated Design-Make-Test-Analyze (DMTA) platform combining generative AI, ultra-large synthetically accessible chemical spaces, physics-informed molecular representations, off-target prediction, and experimental feedback. The developed methods will be applied in iterative prospective drug-discovery cycles, with a serine protease from the complement system serving as a real-world lead-optimization case study. The successful candidate will play a central role in the computational and AI components of the project and work closely with our international and industrial project partners.

You will be responsible for: • Developing and adapting machine-learning approaches for structure-based and generative molecular design.

• Integrating physicochemical information, including protein-ligand interaction features, into generative AI workflows.
• Developing computational workflows for closed-loop DMTA cycles in which experimental affinity, selectivity, and molecular-property data are continuously used to improve the next generation of proposed molecules.
• Applying and validating the developed approaches prospectively in the design and optimization of serine protease inhibitors.
• Collaborating closely with computational scientists, chemists, and biologists within the international project consortium.
• Contributing to scientific publications, presentations, and project reporting.