Head of Computational-Aided Drug Discovery

Reference: BH-69894
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. They are now looking for an outstanding computational scientist to lead their computational group and shape how modern computational methods are applied across the drug discovery portfolio.

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 doingLeading, directing and developing the computational chemistry and data science group, setting scientific strategy and best practice standardsActing as the computational lead embedded within multidisciplinary project teams, generating and prioritising design ideas, resolving SAR and accelerating lead optimisationExploiting structure enabled and fragment based methodology, integrating experimental structural and biophysical data with modelling and designChampioning the appropriate use of machine learning, generative design and AI in drug discovery, including through collaborative AI driven drug discovery initiativesOverseeing cheminformatics and data science workflows that support robust, data driven decision making at every stage of a projectLeading the analysis and exploitation of large scale screening and structural datasets to turn them into actionable insight for hit identification and designDirecting the selection, development and deployment of computational tools and workflows, both commercial and in-houseLine managing, mentoring and growing the team, recruiting as it expandsRepresenting computational science to partners and collaborators, building confidence in the science behind deliveryKeeping abreast of scientific and technological advances and driving adoption of new methods where they genuinely strengthen discovery outcomesWhat we're looking forPhD (or equivalent) in computational chemistry, computer-aided drug discovery, cheminformatics or a related disciplineSignificant, demonstrable experience of computational drug discovery gained within a biotech or pharma environment, not solely academic, including experience leading or directing computational workDeep, hands-on expertise across core computational chemistry methods: molecular docking, virtual screening, molecular dynamics and quantum mechanical calculationsPractical experience applying machine learning and modern AI approaches to real drug discovery problems, with a clear understanding of their strengths and limitationsA demonstrable track record of using computational methods to help drug discovery project teams succeed, with tangible contributions to lead optimisation and candidate nominationA strong track record of using experimental structural information (X-ray, ideally NMR) and biophysical data to drive structure based molecular designProven ability to lead and develop scientists, set direction and manage the priorities and delivery of a technical groupExcellent communication and interpersonal skills, with the ability to explain complex computational science to multidisciplinary teams and external partnersNice to haveHands-on experience of computational approaches within a fragment based drug discovery settingExperience with free energy perturbation (FEP) and other advanced predictive methods for potency and selectivityStrong cheminformatics capability, including building or deploying analysis workflows for the wider discovery teamExperience analysing large or high dimensional screening datasets using big data and statistical methodsProgramming and scripting ability (e.g. Python) and experience developing in-house tools or automating computational workflowsExperience across multiple target classes such as kinases, protein to protein interactions, GPCRs and ion channelsFamiliarity with modalities beyond classical small molecules, such as PROTACs, RIPTACs and ADCsExperience of collaborative, partnered or CRO style discovery, delivering to external stakeholdersA record of contribution to the field through publications, patents or presentations