Target Fellow; Postdoctoral Researcher – DNA encoded library; DEL data analysis

Target 2035 Fellow (Postdoctoral Researcher – DNA encoded library (DEL) data analysis)

Supervisor: Prof. Benjamin Haibe-Kains and Matthieu Schapira

Location: University Health Network, Toronto, Canada

Industry mentor: Principal data/AI scientist at Astra Zeneca

Target 2035 aims to unlock a new frontier in drug discovery by generating the data needed to fuel machine-learning and AI methods for ligand discovery.

In this unique training opportunity, the Target 2035 Fellow will be embedded in an industry‑collaborative, international research program and supported by a multidisciplinary project team. The Fellow will be part of the computational modeling team within SGC and will work closely with Pharmaceutical industry experts in DEL technologies to develop an Open-DEL data analysis platform, to generate massive machine learning (ML)-training datasets for thousands of human proteins unprecedented in capabilities and throughput in the academic setting.

The Fellow will also be supported by medicinal chemists, machine learning scientists, automation and software engineers, biochemists, and cell biologists, contributing to cutting‑edge, data‑driven drug discovery efforts alongside academic, industry, and technology partners. In close partnership with experimental biologists, the Fellow will develop innovative and rigorous computational pipelines to maximize the identification of bioactive compounds from DEL screens. This position offers exceptional interdisciplinary training, high‑impact collaboration at the ML‑biology–chemistry interface, and the opportunity to contribute meaningfully to an area of profound medical and societal importance.

• Design, execute, and troubleshoot DEL data processing and hit identification tools, including signal‑to‑noise optimization, cheminformatics and statistical methodology.
• Implement developments into the Target 2035 DEL data processing pipeline.
• Develop ML and pharmacophore models to discover bioactive molecules using refined DEL screening data.
• Apply models for virtual screening of ultra large make‑on‑demand libraries or de novo design to identify novel hits.

• PhD in computer science, bioinformatics, computational chemistry or a related field.
• Strong background in three or more of the following:
  • Computational analysis of large biological and/or chemical datasets.
  • Statistical assessment of large‑scale screening datasets and experience in developing data processing pipelines.
  • Deep expertise in statistics and data quality metrics.
  • Scripting/coding/deep learning for scientific applications.
• Demonstrated ability to design, execute, and interpret analyses and pipelines independently.
• Excellent written and verbal communication skills.
• Track record of high‑quality, peer‑reviewed publications.
• Enthusiasm for interdisciplinary research and collaborative science.

• Experience with large‑scale DEL and/or DNA sequencing data analysis.
• Experience in computational medicinal chemistry.
• Expertise in machine learning applications to chemical bioactivity datasets.

This position is part of the Target 2035 Fellows program, supported through a Mitacs Accelerate Umbrella program. The fellowship is offered as a full‑time postdoctoral appointment for an initial two‑year term, with the possibility of renewal for up to two additional years, subject to performance, funding availability, and program requirements.

The Fellow will be embedded within University Health Network’s research environment and will contribute to collaborative research aligned with Target 2035, a global open science public‑private partnership focused on accelerating early drug discovery through the generation of high‑quality chemical tools and protein–ligand interaction data for artificial intelligence and machine learning. Training is provided through academic supervision, day‑to‑day integration within UHN’s research teams, monthly meetings with an industry mentor, and participation in collaborative projects involving a global team of academic, technology, and industry partners.