Artificial intelligence is rapidly transforming molecular design and drug discovery. 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. A fully funded Postdoctoral position is available 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.
Tasks
- 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.
Requirements
- 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.
Benefits
- 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.
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Das Team
You will work closely with computational scientists, chemists, and biologists within an international project consortium and collaborate with industrial partners.