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: https://doi.org/10.1038/s41467-025-63947-5 https://doi.org/10.1021/acs.jcim.2c01436 https://doi.org/10.1021/acs.jcim.1c01438 https://doi.org/10.1038/s42004-020-0261-x

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.

Your profile

• 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.

We offer you

• 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.

Application / Contact

Please submit your complete application documents, including: motivation letter (max. 1 page) highlighting your research interests, relevant experience, and skills, CV including publication list, PhD certificate or confirmation of expected completion, and contact details of at least two academic references . Applications should be submitted via the University of Basel online recruiting platform. The position is available immediately. You can find out more about our research at: https://pharma.unibas.ch/de/research/research-groups/computational-pharmacy-2155/ For questions, please contact Prof. Markus Lill ( markus.lill@unibas.ch ).