Target 2035 Fellow (Postdoctoral Researcher - Machine Learning for Drug Discovery)

Target 2035 Fellow (Postdoctoral Researcher - Machine Learning for Drug Discovery) | Structural Genomics Consortium Skip to main content About About SGC Members & Technology Partners Governance Open Science Impact SGC Gender Equality Plan SGC Sites & People Global SGC Toronto SGC UNC SGC Karolinska SGC Frankfurt SGC Neuro SGC UCL SGC Melbourne SGC Unicamp Science and Resources Proteins Structure Gallery Protein Production Resources Expression Vectors Constructs Legacy Protein Protocols Chemistry Chemical Probes SGC Chemical Probes Donated Chemical Probes Chemical Handles Chemogenomic Sets Open Data Target Enabling Packages Patient Tissue Platform Open Lab Notebooks Publications Bioinformatics Resources ChemBioPort UbiHub ChromoHub Initiatives Overview Target 2035 Protein Contribution Network AIRCHECK MAINFRAME CACHE Conscience Agora Open Science Trust EUbOPEN Open Chemistry Networks Women’s and Children’s Health Initiative READDI AViDD TREAT AD YCharOS News & Outreach News from SGC Press Releases Blogs Get Involved Participate Donate Careers Contact Us Donate Home About About SGC Members & Technology Partners Governance Open Science Impact SGC Gender Equality Plan SGC Sites & People Global SGC Toronto SGC UNC SGC Karolinska SGC Frankfurt SGC Neuro SGC UCL SGC Unicamp Science and Resources Proteins Structure Gallery Protein Production Resources Expression Vectors Constructs Legacy Protein Protocols Chemistry Chemical Probes SGC Chemical Probes Donated Chemical Probes Chemical Handles Chemogenomic Sets Open Data Target Enabling Packages Patient Tissue Platform Open Lab Notebooks Publications Bioinformatics Resources ChemBioPort UbiHub ChromoHub Initiatives Overview Target 2035 Protein Contribution Network AIRCHECK MAINFRAME CACHE Conscience Agora Open Science Trust EUbOPEN Open Chemistry Networks Women’s and Children’s Health Initiative READDI AViDD TREAT AD YCharOS News & Outreach News from SGC Press Releases Blogs Get Involved Participate Donate Careers Contact Us Donate Search Target 2035 Fellow (Postdoctoral Researcher - Machine Learning for Drug Discovery) Location: On-site: Toronto, Ontario, Canada (the Structural Genomics Consortium) Online: New York, NY (Columbia University) Term: Multi-year project (Estimated 2026–2028) Supervision: Dr. Matthieu Schapira (SGC/UofT) and Dr. Mohammed AlQuraishi (Columbia University). Position Overview We are seeking a highly motivated Post-Doctoral Fellow to lead a cutting-edge project focused on advancing machine learning (ML) methodologies for drug discovery. This project addresses the fundamental challenges of data scarcity and generalization in drug discovery by leveraging large-scale datasets from DNA-encoded library (DEL) and affinity selection mass spectrometry (ASMS) platforms. As a Mitacs Target 2035 post-doctoral fellow , you will work in an interdisciplinary team of computational chemists, ML experts and experimentalists at University of Toronto and Columbia University. The position will be physically located in the Schapira Lab at University of Toronto and will include regular meetings (weekly online and occasional on-site) with the team of Mohammed AlQuraishi at Columbia University. Core Objectives You will be responsible for executing two tightly integrated research objectives: Defining the boundary between in and out of distribution for multi-modal binding affinity prediction deep learning models: Fine-tune transformer-based affinity models on internal experimental ASMS/DEL high-throughput screening data and evaluate how prediction accuracy degrades as binding site distance from the training set increases — establishing the boundary for in-distribution predictions. Identifying data gaps in training sets to guide experimental data generation: Cluster the human proteome by binding site similarity and generate a ranked target list for experimental screening that maximizes coverage and fills data gaps in ML training sets. This list will guide proteome scale efforts at Target-2035 to generate experimental training sets that accelerate a breakthrough in AI-driven hit finding and optimization. Role Responsibilities Design and implement computational pipelines for proteome-scale binding site detection, featurization, and clustering using structural prediction models (OpenFold) and pocket mapping and embedding tools (Fpocket, BioEmu, UniMol). Fine-tune and benchmark transformer-based affinity prediction models (ex: AQAffinity, DrugCLIP) on ASMS and DEL screening datasets; evaluate generalization across structurally distant binding sites. Develop and validate a distance metric for drug binding sites that quantifies the boundary between in-distribution and out-of-distribution prediction regimes. Generate a dynamically updatable, prioritized target list for experimental screening that maximizes proteome coverage and fills data gaps in ML training sets. Collaborate closely with the SGC experimental teams to align computational target selection with experimental tractability constraints. Coordinate with the Schapira Lab (University of Toronto) and the AlQuraishi Lab (Columbia University / OpenFold) on AI-augmented computational chemistry and multi-modal model development integrating structural and binding data. Present findings at internal Target 2035 consortium meetings and external scientific conferences; publish your work in peer-reviewed publications. Qualifications PhD in Computational Chemistry, Computer Science, Bioinformatics, or a related field. Technical Expertise: Strong background in representation learning, foundation models, and multi-fidelity optimization. Domain Knowledge: Familiarity with physics-based molecular modeling. Skills: Experience with large-scale ML training, handling "noisy" experimental data (like DEL) Appointment, Training Program, and Research Environment The fellowship is a full-time postdoctoral appointment for an initial one-year term, renewable for up to two additional years, subject to performance and program requirements. This project operates within an open science framework, ensuring your work has a high impact on the global drug discovery community. Interested candidates should apply with a CV and a cover letter to matthieu.schapira@utoronto.ca A global public-private partnership that supports the discovery of new medicines through open-access research. Careers Contact Us Structural Genomics Consortium. Registered charity 1097737 (England and Wales). Registered company limited by guarantee 04714553 (England and Wales). Except where otherwise noted, all content on this site is licensed under a Creative Commons Attribution 4.0 International license (CC BY 4.0) Be the first to know with our SGC Bulletin! Want to know what we are up to? Leave your e-mail address below and you will receive a monthly source of SGC news from across our six research sites. SIGN UP FOR OUR NEWSLETTER Footer Privacy Policy Legal Information Contact Us Login

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