Team / Research Leader, Computational Drug Design, London, Lausanne

Your Impact

We are seeking a Team / Research Leader specialising in computational chemistry to spearhead the design of novel small molecule therapeutics. This role requires a unique blend of deep scientific expertise and the ability to lead both technical projects and high-performing teams, whilst contributing to or leading active drug discovery programs.

As a pivotal member of the Drug Design team, you will drive complex projects from early discovery through to candidate selection. You will leverage your expertise in strategic leadership and management of multi-program pipelines to deliver life-changing medicines using our cutting-edge AI drug design engine.


What You Will Do

• Strategic Direction: Define the core scientific and technical direction of our small molecule therapeutic programs, ensuring alignment with company goals.
• Program Leadership: Champion the drug discovery pipeline, overseeing external collaborations (with CROs) and leading programs to meet critical go/no-go milestones.
• Innovation & Integration: Establish principles for embedding cutting-edge AI/ML into modern computational chemistry workflows, ensuring seamless integration across ML, Biology, and Chemistry teams.
• Talent Development: Foster a culture of scientific excellence by proactively mentoring, guiding, and evaluating the career development of team members.
• Technical Execution: Apply AI-first approaches to provide technical and strategic directions, and execute drug design programs.
  
  

Skills and Qualifications

Essential:

• PhD in Computational Chemistry, Medicinal Chemistry, Synthetic Chemistry, or a related scientific field.
• 8+ years’ professional experience in either a pharma, biotech or CRO environment working at all stages of the drug discovery process, from hit finding to lead optimisation and candidate selection.
• Proven experience leading computational chemistry strategy and execution for multiple drug discovery programs and target classes, and managing high-performing scientific teams.
• Strong expertise in virtual screening, ligand and structure-based drug design using physics-based and ideally AI/ML methods.
• Proven track record of developing QSAR models using ML or deep learning methods.
• Expertise in writing and implementing Python scripts for analysis and tool development, and/or utilising pipelining tools to support cheminformatics and design workflows.
• Strong knowledge of medicinal chemistry principles including ADMET and DMPK.
• Knowledge of modalities such as PROTACs, molecular glues, PPI inhibitors or ADCs.
• Genuine passion and enthusiasm for applying AI/ML to drug design and how this can transform the field.
• Demonstrated success managing external collaborations (industry/CROs) and working effectively in cross-functional environments.
• Growth mindset, adaptability, and eagerness to learn new concepts and techniques.

Nice to Have:

• Prior experience leveraging machine learning and generative AI methods for hit finding (e.g. ultra-large virtual screening and active learning), molecular design and lead optimisation (e.g. REINVENT, cofolding and AI-powered ligand/pose scoring methods).
• Prior experience of leading drug discovery programs.
• Track record of authorship of relevant scientific manuscripts.