Pfizer Medicine Design focuses on small molecule drug discovery efforts in multiple disease areas. Our location in Cambridge, MA is at the center of biomedical innovation, facilitating collaborations with local universities and biotech companies. We are seeking a highly motivated scientist with machine learning (ML) and computational chemistry expertise, to develop and integrate a next generation AI-first Chemistry Platform, which will accelerate the discovery of revolutionary medicines and create a healthier world for everyone. You will exploit Pfizer's vast trove of proprietary data and institutional knowledge and help us to build an AI-powered small molecule drug discovery platform.
The successful candidate will work with internal and external ML and computational scientists to develop and integrate ML and computational algorithms, supporting all aspects of small molecule drug discovery, ranging from hit identification, to lead optimization and candidate selection. You will have the opportunity to directly contribute to the discovery of medicines for cardiovascular and metabolic diseases, antivirals and other emerging medical needs, by working on project teams of experienced multidisciplinary drug hunters and by applying ML and computational chemistry techniques on these projects. ROLE RESPONSIBILITIES
- Work with internal and external ML and computational scientists to develop, evaluate and integrate ML and computational algorithms
- Identify and investigate novel applications ML approaches in drug discovery programs
- Prepare data and use-cases to benchmark and refine ML algorithms
- Work with experienced medicinal chemists, modelers and software engineers to design UI and API to ensure seamless integration of ML algorithms into the existing internal software platform
- Apply your computational aided drug design skills as an active participant on multiple interdisciplinary project teams to impact molecule design
- Contribute to drug discovery projects by applying ML and molecular modeling to support drug design efforts, using structure-based and ligand-based Hit ID, virtual screening, and lead optimization approaches
- PhD in Computational Chemistry or a related discipline.
- Strong understanding of computational chemistry and physical chemistry
- Applied knowledge of machine learning tools and techniques (e.g. Deep Neural Networks, non-linear multivariate analysis)
- Working knowledge of computer programming languages (e.g. Python, R, Java, Jupyter)
- Practical knowledge of the Linux environment and scripting
- Demonstrated problem solving abilities and good communication/organizational skills within a collaborative team environment.
ADDITIONAL JOB INFORMATION
- Formal training in computer science or applied mathematics
- Experience in manipulating chemical structure data, molecular properties, etc.
- Familiarity with common computational chemistry concepts and techniques (e.g. structure-based and ligand-based drug design, protein-ligand docking, virtual screening, cheminformatics, QSAR, structural bioinformatics, homology modeling, molecular dynamics, free energy perturbation, quantum chemical calculations)
- Experience with common modeling software packages, such as: OpenEye, RDKit, Schrödinger, Chemical Computing MOE, Pipeline Pilot, Gaussian, AMBER
- Experience with common numerical/ML software packages, such as: PyTorch, R, NumPy, etc.
- Relocation support available
- Eligible for employee referral bonus
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Research and Development