Senior Principal Scientist

Company Description

Domainex is a leading drug discovery service company based near Cambridge, UK, providing innovative solutions to pharmaceutical, biotechnology, academic, and patient foundation clients worldwide. With over twenty years of successful drug discovery programs, Domainex delivers scientifically efficient approaches to accelerate drug development. Offering a comprehensive range of services, including medicinal chemistry, computational chemistry, assay development, protein production, and analytical chemistry, the company is equipped to support projects at any stage of drug discovery. Domainex's proprietary technologies, such as FragmentBuilder, LeadBuilder, and Computational Domain Hunting, enable faster and resource-efficient drug discovery processes, saving up to 30% of the time compared to standard industry practices.

The Role

We are seeking an exceptional Senior Principal Scientist/Research Leader in Computational Chemistry. This senior role combines deep scientific expertise with strategic leadership, helping shape both our scientific direction and internal capabilities in computational drug discovery.

Principal Accountabilities

• Strategic Direction: Establish and execute a clear scientific and technical roadmap for the computational chemistry function, that evolves in step with the company growth plans and long term ambitions.
• Program Leadership: Apply a broad suite of computational chemistry methodologies to design and deliver high impact research programs for client sponsored discovery projects.
• BD support: Partner with the BD team in securing new business by providing technical input and enhancing the value of existing work programs through scientific innovation, expanded scope and deeper client collaboration.
• Talent Development: Lead and foster the professional growth of team members through effective mentoring, guidance, and support.
• Innovation and Integration: Establish and embed cutting-edge AI/ML capabilities into computational chemistry workflows, ensuring seamless integration and collaboration with biology and bioanalytical teams.

Educational Background

• A PhD in Computational Chemistry is an essential requirement for this role.

Skills and Experience Required

• Hold >5 years of experience as a computational chemist within a professional drug design environment or organisation.
• Proven experience leading computational drug discovery strategies and contributing across all stages of the discovery process, from hit identification through lead optimisation to candidate selection.
• Hands-on experience with industry-standard molecular modelling software packages, e.g. Schrödinger Suite (Maestro, Glide, FEP+, etc.), and/or equivalent platforms.
• Strong experience in virtual screening, ligand- and structure-based drug design, and the application of physics-based and AI/ML methods.
• Expertise in molecular modelling, free energy methods, docking, QSAR/ML or deep learning approaches and modern cheminformatic platforms.
• Strong knowledge of medicinal chemistry principles, including ADMET, and a solid understanding of emerging modalities such as PROTACs, molecular glues, and protein–protein interaction inhibitors.
• Expertise in developing and implementing pipeline tools to support cheminformatics and molecular design workflows.
• High proficiency in Python for scientific programming, workflow automation, and data analysis.
• Strong experience managing reproducible computational environments using Conda (or equivalent), including environment creation, maintenance, and deployment across teams and HPC systems.
• Experience with Linux and HPC.
• Effective verbal and written communication skills.
• Ability to work effectively within multidisciplinary teams, partnering closely with medicinal chemists, biologists and structural biologists, and to communicate complex analyses clearly to diverse audiences.
• Track record of training and mentoring junior computational chemists.
• Strong scientific curiosity and a passion for pushing the boundaries of computational methods in drug design.