About the job
As part of our expansion to Cambridge, MA, we are seeking an experienced and strategic Head of Computational Drug Design to join our foundational Drug Design team. This is a pivotal role for a motivated scientific leader eager to shape the future of drug discovery at the intersection of computational chemistry and artificial intelligence. 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.
Responsibilities
Apply your knowledge and track record of modern drug design and discovery principles to deliver our Drug Design portfolio.
Provide direct line management for a growing team of Computational Chemists. Set clear goals, provide regular feedback, support their professional development, and foster a collaborative and high-performance team environment.
Provide strategic input and technical direction to our AI/ML platform teams.
Collaborate both internally and with third party organisations, interacting within multidisciplinary teams, to support data-driven delivery of high quality scientific solutions to complex technical problems.
Represent the company externally, interacting with existing and prospective collaborators and Contract Research Organizations (CROs).
Prepare detailed scientific proposals and reports to support new and ongoing programs.
Embrace and champion our culture of inclusion and continuous professional development.
Qualifications
Minimum
PhD in Computational Chemistry, Medicinal Chemistry, Synthetic Chemistry, or a related scientific field, or equivalent industrial experience.
Solid foundation in computational chemistry with a minimum of 10+ years previous industrial experience within a pharma/biotech/CRO setting.
Proven experience leading computational chemistry strategy and execution for multiple drug discovery programs and target classes, and managing high-performing scientific teams.
Prior experience of leading drug discovery programs, from hit finding to lead optimisation and candidate selection.
Strong expertise in virtual screening, ligand and structure-based drug design using physics-based and ideally AI/ML methods.
Proven track record of developing 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.
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.
Preferred
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
Strong knowledge of drug design principles, including ADMET and DMPK.
Knowledge of modalities such as PROTACs, molecular glues, PPI inhibitors or ADCs.
A track record of authorship of relevant scientific manuscripts.