Postdoctoral Position in AI-Driven Drug Design

Universität Basel; Basel; Vor Ort; CHF 90.000 - 110.000; Vollzeit

Zusammenfassung

Universität Basel recruits a fully funded Postdoctoral researcher to advance AI-driven drug design within an international DMTA project. The role focuses on developing ML methods for structure-based and generative design, integrating physicochemical features, and validating approaches in prospective drug-discovery cycles with serine protease inhibitors as a lead case.

The successful candidate will collaborate across computational chemistry, chemistry and biology teams, contribute to

Qualifikationen

• 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 molecular generative AI, cheminformatics and molecular representations, structure-based drug design and protein-ligand modeling.
• A strong publication record in internationally recognized venues (e.g., JCTC, ICML, NeurIPS).
• Fluent English (verbal and written).

Aufgaben

• Develop machine-learning approaches for structure-based and generative molecular design.
• Integrate physicochemical information, including protein-ligand interactions, into generative AI workflows.
• Develop workflows for closed-loop DMTA cycles using experimental data to improve designs.
• Apply and validate approaches prospectively for serine protease inhibitors.
• Collaborate with computational scientists, chemists and biologists within the project consortium.
• Contribute to scientific publications, presentations and project reporting.

Kenntnisse

Machine learning

Deep learning

Python programming

Drug design

Protein-ligand modeling

Ausbildung

PhD in Computational Chemistry

Tools

Python

Molecular modeling

Jobbeschreibung

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.

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 ([e-mail]).