Postdoctoral Position In Ai-driven Drug Design

ScholarLink Inc.; Basel; Hybrid; CHF 90,000 - 110,000; Full time

Job summary

University of Basel invites applications for a fully funded Postdoctoral position in AI-driven drug design within the Computational Pharmacy group. The role focuses on developing and applying machine-learning and physics-informed methods in an integrated Design–Make–Test–Analyze (DMTA) platform for iterative drug discovery.

Collaboration spans international partners and industrial collaborators. The project centers on an AI-driven closed-loop workflow with a serine protease as a real-world lead

Qualifications

• PhD in a computational field relevant to drug design.
• Experience with structure-based drug design and generative models.
• Experience implementing closed-loop DMTA workflows and validating designs.

Responsibilities

• Develop and adapt machine-learning approaches for structure-based and generative molecular design.
• Integrate physicochemical information, including protein–ligand interaction features, into AI workflows.
• Develop computational workflows for DMTA cycles using experimental data to iteratively improve molecule design.
• Collaborate with chemists, biologists, and international project partners.
• Contribute to scientific publications and project reporting.

Skills

Machine learning

Structure-based design

Generative chemistry

Protein–ligand interactions

DMTA workflows

Education

PhD in Computational

Tools

Molecular modeling software

Job description

# Postdoctoral Position in AI-Driven Drug DesignPostdoc position·Post Doctoral·SwitzerlandPosition type: Postdoc postdoctoralLevel: Post DoctoralLanguage: English## About the PositionArtificial 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/s41467-025-63947-5https://doi.org/10.1021/acs.jcim.2c01436https://doi.org/10.1021/acs.jcim.1c01438https://doi.org/10.1038/s42004-020-0261-xYour positionA 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 profilePhD in Computational## Other Requirements & Additional InfoEmployment type: FULL\\_TIME