Head of Computational-Aided Drug Discovery

Up to £100,000 Hybrid based in Cambridge

About the company

Our client is a well established, science led drug discovery company with a strong track record of delivering pre-clinical candidates for partners on challenging targets. They combine deep in-house structural and biophysical expertise (X-ray crystallography, NMR and a full suite of biophysical methods) with modern computational chemistry, cheminformatics, machine learning and AI, including structure enabled and fragment based approaches to hit and lead generation.

The role

Reporting to a senior chemistry leader, this is a senior scientific leadership position with genuine influence over how computational insight translates into project progress for the business and its partners. It is a hands-on role: you will set the scientific direction and standards for computational science, line manage and grow a small, growing team of computational chemists and data scientists, and stay closely embedded in live projects, applying the methods yourself where it matters most.

You will work alongside medicinal, synthetic and analytical chemists, protein scientists, structural biologists, biophysicists and biologists, guiding projects from hit identification through to candidate nomination.

What you'll be doing

• Leading, directing and developing the computational chemistry and data science group, setting scientific strategy and best practice standards
• Acting as the computational lead embedded within multidisciplinary project teams, generating and prioritising design ideas, resolving SAR and accelerating lead optimisation
• Exploiting structure enabled and fragment based methodology, integrating experimental structural and biophysical data with modelling and design
• Championing the appropriate use of machine learning, generative design and AI in drug discovery, including through collaborative AI driven drug discovery initiatives
• Overseeing cheminformatics and data science workflows that support robust, data driven decision making at every stage of a project
• Leading the analysis and exploitation of large scale screening and structural datasets to turn them into actionable insight for hit identification and design
• Directing the selection, development and deployment of computational tools and workflows, both commercial and in-house
• Line managing, mentoring and growing the team, recruiting as it expands
• Representing computational science to partners and collaborators, building confidence in the science behind delivery
• Keeping abreast of scientific and technological advances and driving adoption of new methods where they genuinely strengthen discovery outcomes

What we're looking for

• PhD (or equivalent) in computational chemistry, computer-aided drug discovery, cheminformatics or a related discipline
• Significant, demonstrable experience of computational drug discovery gained within a biotech or pharma environment, not solely academic, including experience leading or directing computational work
• Deep, hands-on expertise across core computational chemistry methods: molecular docking, virtual screening, molecular dynamics and quantum mechanical calculations
• Practical experience applying machine learning and modern AI approaches to real drug discovery problems, with a clear understanding of their strengths and limitations
• A demonstrable track record of using computational methods to help drug discovery project teams succeed, with tangible contributions to lead optimisation and candidate nomination
• A strong track record of using experimental structural information (X-ray, ideally NMR) and biophysical data to drive structure based molecular design
• Proven ability to lead and develop scientists, set direction and manage the priorities and delivery of a technical group
• Excellent communication and interpersonal skills, with the ability to explain complex computational science to multidisciplinary teams and external partners

Nice to have

• Hands-on experience of computational approaches within a fragment based drug discovery setting
• Experience with free energy perturbation (FEP) and other advanced predictive methods for potency and selectivity
• Strong cheminformatics capability, including building or deploying analysis workflows for the wider discovery team
• Experience analysing large or high dimensional screening datasets using big data and statistical methods
• Programming and scripting ability (e.g. Python) and experience developing in-house tools or automating computational workflows
• Experience across multiple target classes such as kinases, protein to protein interactions, GPCRs and ion channels
• Familiarity with modalities beyond classical small molecules, such as PROTACs, RIPTACs and ADCs
• Experience of collaborative, partnered or CRO style discovery, delivering to external stakeholders
• A record of contribution to the field through publications, patents or presentations