We are seeking a highly motivated and creative Computational Medicinal Chemist to support discovery programs. The successful candidate will operate at the interface of computational chemistry and medicinal chemistry, serving as an embedded, project-facing contributor within lead identification and lead optimisation (LI/LO) teams, with additional involvement in earlier discovery phases.

This role is strongly project-driven: you will apply computational approaches to directly inform compound design, optimisation, and decision-making, working closely with multidisciplinary teams in chemistry, biology, and data science. The position offers the opportunity to make a tangible impact on the design and optimisation of novel chemical matter, leveraging the company’s core expertise degrader discovery.

• Serve as a core computational contributor on project teams, supportinglead identification, hit-to-lead, and lead optimisation through structure- and ligand-based design.
• Apply and integrate computational techniques such asmolecular docking, ligand-based design, diversity and conformational analyses, molecular modelling, and related approaches to guide SAR exploration and compound optimisation.
• Contribute to thedesign and prioritisation of project-relevant compound libraries and collections in close collaboration with medicinal chemists.

• Communicate computational results clearly and effectively to cross-functional teams, translating analyses into actionable medicinal chemistry guidance.

• Contribute to the continuous improvement of computational workflows, tools, and best practices.

• PhD in computational chemistry, chemistry, biophysics, or a related scientific discipline with a strong computational focus.
• 1-4 years of Postdoctoral training and/or relevant industry experience in pharmaceutical or biotech drug discovery .
• Demonstrated application of ligand- and structure-based design approaches in small-molecule discovery projects.
• Working knowledge of medicinal chemistry and preclinical drug design in the context of compound optimisation, including SAR development, ADME considerations, and multiparameter optimisation.
• Solid understanding of protein–ligand interactions and structure-based compound optimisation strategies, along with hands-on experience with multiple core computational techniques in ligand- and structure-based drug design.
• Proficiency in Python and cheminformatics toolkits with practical application of these skills for scientific scripting, data analysis, and workflow development.
• Familiarity with machine-learning approaches for small-molecule activity and ADME property prediction, and experience applying model outputs in a medicinal chemistry or project context.
• Experience working effectively within multidisciplinary project teams.

• Experience in targeted protein degradation is a plus