Supervisor: Cheryl Arrowsmith
Program: Mitacs Accelerate Umbrella - Target 2035 Fellows
Position Summary
We are seeking a highly motivated Postdoctoral Researcher with experience in X-ray Crystallography to join an ambitious, data-driven drug discovery program at the interface of structural biology, chemical biology, and artificial intelligence. This role will play a central part in hit characterisation for LIGAND-AI, generating high-quality protein-ligand co-crystal structures that directly inform structure-based drug discovery research programs, the development of machine learning models and enable international benchmarking challenges.
The Postdoctoral Researcher will focus on structure determination of protein-ligand complexes spanning key biological pathways including epigenetics, ubiquitin signalling, gene regulation, and DNA repair. These structures will not only drive internal hit validation and SAR development but will also feed into global open science efforts and benchmarking initiatives such as CASP, helping define the next generation of AI-enabled drug discovery.
This is an exciting opportunity to work at the frontier of structural biology and AI, where experimentally rigorous crystallographic data are treated as first-class training data for machine learning. The successful candidate will contribute to a growing portfolio of high-impact structures designed to stress-test, validate, and improve LIGAND-AI predictions in real-world drug discovery settings.
The Postdoctoral Researcher will be embedded in a highly collaborative early-stage drug discovery team, working closely with medicinal chemists, computational scientists, machine learning experts, automation engineers, and biochemists. Together, the team
integrates structure, chemistry, and data science to rapidly advance hit matter and generate benchmark-quality datasets with broad community value.
Key Responsibilities
- Lead X-ray crystallography efforts for hit characterisation, including crystallisation, ligand soaking/co-crystallisation, data collection, and structure refinement
- Generate high-resolution protein-ligand complex structures to interrogate ligand hits and validate predictions, helping to guide SAR development
- Design, execute, and troubleshoot experiments across protein expression, purification, and biophysical characterisation as needed to support structural work
- Analyse and interpret crystallographic data in close collaboration with medicinal chemists and computational scientists to inform compound optimisation
- Contribute structurally rigorous datasets to AI benchmarking and open science initiatives (e.g., CASP, Beacon, Target 2035)
- Maintain high-quality experimental records using electronic laboratory notebooks, ensuring data and metadata are reproducible, shareable, and publication-ready
- Present results clearly in interdisciplinary project meetings and contribute to manuscripts, preprints, and public data releases
- Foster a collaborative, rigorous, and open research culture aligned with best practices in modern structural biology
The positions offer a highly collaborative environment, access to state-of-the-art infrastructure, and the opportunity to contribute to research addressing a major unmet medical and societal need.
Qualifications
Required:
- PhD awarded within the previous 5 years in structural biology, biochemistry, chemical biology, or a related field
- Strong hands-on experience in X-ray crystallography, including structure determination of protein-ligand complexes
- Solid background in protein expression and purification, as well as biophysical chemistry methods
- Demonstrated ability to independently design, execute, and interpret experiments
- Excellent written and verbal communication skills
- Track record of high-quality, peer-reviewed publications
- Enthusiasm for interdisciplinary, collaborative, and open science research
Preferred:
- Experience using structural data to guide hit validation, SAR, or early-stage drug
discovery - Familiarity with ligandability assessment or fragment-based approaches
- Interest in AI/ML applications in structural biology and drug discovery
- Experience contributing data to shared resources, consortia, or benchmarking efforts
Appointment, Training Program, and Research Environment
This position is part of the Target 2035 Fellows program, supported through a Mitacs Accelerate Umbrella award. The appointment is a temporary full-time postdoctoral position for an initial one-year term, with the possibility of renewal for up to two additional years, subject to performance, funding availability, and program requirements.
The Postdoctoral Researcher will be based at University Health Network (UHN) within the Princess Margaret Cancer Centre research environment and will contribute to Target 2035, a global public-private partnership dedicated to accelerating early drug discovery through the open generation of high-quality protein-ligand interaction data for AI/ML.
Training will occur through close academic supervision, day-to-day integration within UHN research teams, and active participation in international, multidisciplinary collaborations spanning academia, industry, and technology partners. The role offers exceptional exposure to AI-native drug discovery, state-of-the-art structural biology infrastructure, and high-visibility scientific outputs.
Find more information and apply here: https://jobs.smartrecruiters.com/UniversityHealthNetwork/744000109020350-postdoctoral-researcher-structural-biology