As the world's leading pharmaceutical company, Pfizer is uniquely positioned with some of the largest and most complex chemical and biological data sets available anywhere. The Computational Absorption, Distribution, Metabolism, and Excretion (cADME) group at Pfizer is responsible for maximizing the value of ADME, Safety, and Pharmacology data generated to support drug discovery project teams. The Biotransformation group provides issue-driven drug metabolism expertise, including metabolite identification, structural elucidation, and interpretation of metabolic pathways, to support project teams across drug discovery and development.
We are seeking a highly motivated postdoctoral fellow with expertise in machine learning (ML) and artificial intelligence (AI) to develop predictive models for small-molecule drug metabolism. The role offers a unique opportunity to apply advanced ML/AI approaches to real-world pharmaceutical data at unprecedented scale, in close collaboration with experts in biotransformation, drug safety, and computational ADME. The fellow will have the opportunity to work with large-scale experimental metabolite identification (MetID) data, leveraging Pfizer's extensive, curated in vitro metabolism data assets to enable data-driven prediction of metabolic sites, metabolite structures, and transformation pathways.
This project aims to develop interpretable, mechanistically grounded ML models trained on empirical metabolite structure and abundance data and to integrate these predictive capabilities into AI-enabled decision-support workflows for drug discovery and development. This role will work closely with metabolism experts in real-time, testing and validating these models as experimental data is generated. The models developed in this project will be immediately impactful on internal decision making as well as influence the scientific field and regulatory landscape through external publication and application to ongoing evaluations.
Key Responsibilities
Required Qualifications
Preferred Qualifications
Additional Information
Tagged as: Life Sciences
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