Pfizer’s Machine Learning and Computational Sciences group is looking for a computational chemist experienced in machine learning to develop predictive and generative models for ligand-based and structure-based small molecule drug design. This effort will leverage large datasets (including structure-activity relationship databases, and X-ray/cryo-EM structures) that are exclusively available at Pfizer. The successful candidate will explore physics-inspired deep learning approaches that can learn from these datasets and generate and accurately score new designs.
This role will collaborate closely with the broader Medicinal Sciences organization at Pfizer to prospectively test developed models on active discovery campaigns. This role will also collaborate with Pfizer’s hub for machine learning, and cheminformatics, MLOps, and HPC teams to train and deploy models. The incumbent is expected to join academic collaborations, publish in reputed journals, and present at high-profile conferences. Our group applies machine learning methods to multiple therapeutic modalities, so while the role initially focuses on small molecules, the incumbent will have the opportunity to branch out into vaccines, mRNA-based therapies, and biologics.
Instructions on how to apply can be found HERE.