Senior Scientist in Structural Biology at Vernalis (R&D) Ltd, Cambridge, UK

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Our scientists leverage cutting-edge lead generation and structure-based design to unlock new possibilities in drug discovery, advancing innovative compounds towards development candidates across diverse therapeutic areas, including oncology, neurodegeneration, anti-infectives and inflammation. The Role – job Summary We are seeking a highly motivated Senior Scientist in structural biology to support our drug discovery programs. Your expertise in protein crystallography will be instrumental in solving multiple ligand-bound protein structures, understanding protein-ligand interactions to accelerate the identification and optimization of high-quality lead compounds. This is a hands-on role covering the full spectrum of a protein crystallography study including construct design, protein crystallisation, crystallographic data collection, structure solution and refinement, structural analysis, and reporting. You will contribute to hit-identification and validation strategies and help enable high-throughput structure-determination workflows. You will collaborate across multiple disciplines including computational chemistry, biophysics, and medicinal chemistry teams with both internal and external collaborators and play a key part in shaping research directions and advancing structure-guided drug discovery projects. Key Responsibilities Use protein crystallography to solve high-resolution apo and ligand-bound structures of protein targets. Identify potential binding sites, interpret ligand-binding mechanisms, and optimise small-molecule interactions with protein targets. Identify starting points for drug development. Design, execute and interpret high-throughput crystallography screens of compound libraries, followed by optimisation and hit expansion. Integrate structural insights into the wider drug discovery pipeline, working closely with medicinal chemistry, biophysics and computational biology teams. Provide structural rationale for the design and modification of small molecules that target key proteins involved in disease. Stay at the forefront of new structural biology methodologies, particularly in ligand binding and FBDD. Develop and optimise protocols to increase throughput, improve structural data quality and streamline workflows. Collaborate cross-functionally with internal teams and external partners across drug discovery projects. Present project data, scientific interpretation and strategy internally and to external collaborators. Represent Vernalis at scientific conferences. Assist in the training and supervision of students and junior team members. About You Essential Qualifications & Experience: PhD in Structural Biology, Biophysics or a related field, with postdoctoral or industrial experience in drug discovery and structural biology techniques, particularly in relation to protein–ligand interactions and fragment-based drug discovery. Experience in high-throughput crystallographic screens and the use of advanced software, such as COOT, CCP4i2, PanDDA, to determine and refine large numbers of protein-ligand complexes. Experience in rapid fitting of ligands to density maps, with consideration of ligand occupancy states and alternative conformation analysis. Strong understanding of ligand chemistry including ligand geometries, low-energy conformations, chirality, protonation and tautomeric states, and bond orders. Experience in protein crystallisation screening experiments using robotics and commercial crystallisation screens. Experience in protein-ligand complex formation by co-crystallisation and soaking experiments, including cryoprotectant determination and cryo-freezing for in-house and synchrotron data collection. Experience in protein construct design for crystallisation studies. Ability to work effectively in a dynamic, fast-paced research environment. Excellent communication, collaboration and project-management skills. Desirable: Experience of cryo-EM sample preparation and data analysis. Familiarity with protein expression, purification, and characterization workflows. Computational skills for integrating structural data with molecular modelling, docking, and simulation techniques to drive drug design efforts. Experience applying deep Learning approaches to data analysis. Skills and competencies Essential: Strong hands-on experimental capability and troubleshooting skills. Excellent data-analysis and scientific-interpretation skills. Ability to translate complex data into clear, actionable insights. Effective communication across multidisciplinary teams. Desirable: Leadership and mentoring experience Demonstratable ability to innovate What We Offer The opportunity to play a key role in advancing drug discovery through cutting-edge structural biology. A collaborative, cross-functional environment with talented scientists across structural biology, medicinal chemistry, biophysics and computational biology. Access to well-equipped laboratories, advanced technologies and a range of drug discovery approaches. Opportunities for personal and professional growth within a dynamic and growing drug discovery organisation. Career development in an international research environment. Competitive salary, flexible working arrangements and a supportive, inclusive work culture. Comprehensive benefits package, including private healthcare, pension contributions and generous holiday entitlement. How to Apply To apply, please submit your CV and a cover letter outlining your relevant experience and motivation for the role to HRE@vernalis.com Applications will be reviewed on a rolling basis, so early application is encouraged. Vernalis is an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. 17COUNTRIES & MEMBERS 11CENTRES 29FACILITIES 104SERVICES 13089 USERS 2214 PUBLICATIONS Instruct-ERIC Hub, Oxford House, Parkway Court, John Smith Drive, Oxford OX4 2JY +44 (0) 1865 98 86 39 | support@instruct-eric.org | Acknowledgements | Powered by ARIA Terms of Use | Terms of Submission | Privacy Policy | Access Policy | Copyright © 2026 The University of Oxford This platform benefited from services and the resources provided by EGI, with the dedicated support of IN2P3-IRES