Scientist , computational chemist - CADD

Scientist, Computational Chemist - CADD

Location: Basel, Switzerland

We are seeking a highly motivated, creative Computational Medicinal Chemist to support drug 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. The position will also involve contributions to earlier drug discovery phases and the development of computational tools with direct application in medicinal chemistry.

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 in degrader discovery.

Responsibilities include:

·         Serve as a core computational contributor on project teams, supporting lead identification, hit-to-lead, and lead optimisation through structure- and ligand-based design.

·         Apply and integrate computational techniques such as molecular docking, ligand-based design, diversity and conformational analyses, molecular modelling, and related approaches to guide SAR exploration and compound optimisation.

·         Contribute to the design 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 development and continuous improvement of computational workflows, tools, and best practices within the realm of computational medicinal chemistry.

Required skills and experience

PhD in computational chemistry, chemistry, biophysics, or a related scientific discipline with a strong computational focus.
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 drug 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.
·         Hands-on experience with molecular visualisation and modelling software such as PyMOL and Schrödinger suite for structure analysis and drug design applications.

·         Proficiency in Python and cheminformatics toolkits with practical application of these skills for scientific scripting, data analysis, workflow development, and management and analysis of large chemical datasets.

·         Proficiency in statistical analysis and data visualisation tools (e.g. Jupyter notebooks, Spotfire).

·         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 and communicating computational results to non-computational scientists.
·         Strong scientific writing skills and track record of publications in peer-reviewed journals.

Experience in targeted protein degradation is a plus.

Interested Candidates may forward a CV and Cover Letter in a single PDF via our online portal https://www.monterosatx.com/careers/

Monte Rosa Therapeutics is a clinical-stage biotechnology company developing highly selective molecular glue degrader (MGD) medicines for patients living with serious diseases in the areas of oncology, autoimmune and inflammatory diseases, and more. MGDs are small-molecule protein degraders that have the potential to treat many diseases that other modalities, including other degraders, cannot. Monte Rosa’s QuEEN™ (Quantitative and Engineered Elimination of Neosubstrates) discovery engine combines AI-guided chemistry, diverse chemical libraries, structural biology, and proteomics to rationally design MGDs with unprecedented selectivity. Monte Rosa has developed the industry’s leading pipeline of MGDs, which spans autoimmune and inflammatory diseases, oncology, and beyond. Monte Rosa has a global license agreement with Novartis to advance VAV1-directed molecular glue degraders and a strategic collaboration with Roche to discover and develop MGDs against targets in cancer and neurological diseases previously