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:

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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.

• PhD in Computational Chemistry, Cheminformatics, Computer Science, Physics, or a related discipline.
• Strong background in machine learning and deep learning.
• Strong programming skills, particularly in Python.
• Experience in at least one of the following areas:
• molecular generative AI,
• cheminformatics and molecular representations,
• structure-based drug design and protein-ligand modeling,
• Experience with molecular modeling and a good understanding of the physicochemical principles governing molecular recognition is highly desirable.
• A strong publication record in internationally recognized, high-quality venues is required, such as leading journals in computational chemistry (e.g., JCTC, Journal of Chemical Physics) or top-tier machine-learning conferences (e.g., ICLR, ICML, NeurIPS), as appropriate to the candidate's research background.
• Fluent verbal and written communication skills in English.
• Highly motivated, independent, and collaborative researcher with an interest in working at the interface between methodological development and prospective drug discovery.

• A Postdoctoral position in an interdisciplinary research project at the interface of artificial intelligence and drug discovery.
• The opportunity to develop new computational methodologies and directly test them in prospective Design-Make-Test cycles.
• Close interaction with experimental drug-discovery researchers and industrial and international project partners.
• An international and collaborative research environment at the University of Basel.

You can find out more about our research at: /